{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from linearmodels.panel import compare\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from linearmodels.panel import PanelOLS, RandomEffects\n",
    "from scipy.stats import chi2\n",
    "from statsmodels.stats.diagnostic import het_breuschpagan\n",
    "from statsmodels.stats.stattools import durbin_watson"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.Data Loading & Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "file_path = '../data/CA2_filled.csv'\n",
    "data = pd.read_csv(file_path)\n",
    "\n",
    "# Data preprocessing: dependent and independent variables\n",
    "# Dependent variable: log(GDP)\n",
    "# Independent variables: edu_m, edu_f, gra_m, gra_f, gee, er_m, er_f, law, cpi\n",
    "\n",
    "# Convert variables to appropriate data types (ensure correctness)\n",
    "data['Country'] = data['Country'].astype('category')\n",
    "data['Year'] = data['Year'].astype('int')\n",
    "\n",
    "# Apply log transformation to gdp\n",
    "data['log_gdp'] = np.log(data['gdp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['gpi'] = data['edu_f'] / data['edu_m']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Year</th>\n",
       "      <th>gdp</th>\n",
       "      <th>gni</th>\n",
       "      <th>edu_m</th>\n",
       "      <th>edu_f</th>\n",
       "      <th>edu</th>\n",
       "      <th>gra_m</th>\n",
       "      <th>gra_f</th>\n",
       "      <th>gee</th>\n",
       "      <th>er_m</th>\n",
       "      <th>er_f</th>\n",
       "      <th>er</th>\n",
       "      <th>law</th>\n",
       "      <th>cpi</th>\n",
       "      <th>log_gdp</th>\n",
       "      <th>gpi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>China</td>\n",
       "      <td>2004</td>\n",
       "      <td>3061.833173</td>\n",
       "      <td>4420</td>\n",
       "      <td>11.495247</td>\n",
       "      <td>9.856013</td>\n",
       "      <td>10.675630</td>\n",
       "      <td>5.970477</td>\n",
       "      <td>5.209082</td>\n",
       "      <td>2.561830</td>\n",
       "      <td>77.076</td>\n",
       "      <td>65.253</td>\n",
       "      <td>71.230</td>\n",
       "      <td>0</td>\n",
       "      <td>3.824637</td>\n",
       "      <td>8.026769</td>\n",
       "      <td>0.857399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>China</td>\n",
       "      <td>2005</td>\n",
       "      <td>3390.716159</td>\n",
       "      <td>5020</td>\n",
       "      <td>11.743086</td>\n",
       "      <td>10.133878</td>\n",
       "      <td>10.938482</td>\n",
       "      <td>6.576500</td>\n",
       "      <td>5.935305</td>\n",
       "      <td>2.597241</td>\n",
       "      <td>76.593</td>\n",
       "      <td>64.575</td>\n",
       "      <td>70.652</td>\n",
       "      <td>0</td>\n",
       "      <td>1.776414</td>\n",
       "      <td>8.128796</td>\n",
       "      <td>0.862966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>China</td>\n",
       "      <td>2006</td>\n",
       "      <td>3800.765796</td>\n",
       "      <td>5830</td>\n",
       "      <td>11.996267</td>\n",
       "      <td>10.442923</td>\n",
       "      <td>11.219595</td>\n",
       "      <td>7.379805</td>\n",
       "      <td>6.775434</td>\n",
       "      <td>2.632324</td>\n",
       "      <td>76.208</td>\n",
       "      <td>63.960</td>\n",
       "      <td>70.154</td>\n",
       "      <td>0</td>\n",
       "      <td>1.649431</td>\n",
       "      <td>8.242958</td>\n",
       "      <td>0.870514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>China</td>\n",
       "      <td>2007</td>\n",
       "      <td>4319.031398</td>\n",
       "      <td>6840</td>\n",
       "      <td>12.243160</td>\n",
       "      <td>10.718593</td>\n",
       "      <td>11.480876</td>\n",
       "      <td>8.127824</td>\n",
       "      <td>7.717484</td>\n",
       "      <td>2.700735</td>\n",
       "      <td>75.805</td>\n",
       "      <td>63.331</td>\n",
       "      <td>69.640</td>\n",
       "      <td>0</td>\n",
       "      <td>4.816768</td>\n",
       "      <td>8.370786</td>\n",
       "      <td>0.875476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>China</td>\n",
       "      <td>2008</td>\n",
       "      <td>4711.643449</td>\n",
       "      <td>7630</td>\n",
       "      <td>12.594070</td>\n",
       "      <td>11.025141</td>\n",
       "      <td>11.809605</td>\n",
       "      <td>8.948138</td>\n",
       "      <td>8.781254</td>\n",
       "      <td>3.631788</td>\n",
       "      <td>75.112</td>\n",
       "      <td>62.514</td>\n",
       "      <td>68.886</td>\n",
       "      <td>0</td>\n",
       "      <td>5.925251</td>\n",
       "      <td>8.457792</td>\n",
       "      <td>0.875423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country  Year          gdp   gni      edu_m      edu_f        edu     gra_m  \\\n",
       "0   China  2004  3061.833173  4420  11.495247   9.856013  10.675630  5.970477   \n",
       "1   China  2005  3390.716159  5020  11.743086  10.133878  10.938482  6.576500   \n",
       "2   China  2006  3800.765796  5830  11.996267  10.442923  11.219595  7.379805   \n",
       "3   China  2007  4319.031398  6840  12.243160  10.718593  11.480876  8.127824   \n",
       "4   China  2008  4711.643449  7630  12.594070  11.025141  11.809605  8.948138   \n",
       "\n",
       "      gra_f       gee    er_m    er_f      er  law       cpi   log_gdp  \\\n",
       "0  5.209082  2.561830  77.076  65.253  71.230    0  3.824637  8.026769   \n",
       "1  5.935305  2.597241  76.593  64.575  70.652    0  1.776414  8.128796   \n",
       "2  6.775434  2.632324  76.208  63.960  70.154    0  1.649431  8.242958   \n",
       "3  7.717484  2.700735  75.805  63.331  69.640    0  4.816768  8.370786   \n",
       "4  8.781254  3.631788  75.112  62.514  68.886    0  5.925251  8.457792   \n",
       "\n",
       "        gpi  \n",
       "0  0.857399  \n",
       "1  0.862966  \n",
       "2  0.870514  \n",
       "3  0.875476  \n",
       "4  0.875423  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.Before Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (1)Descriptive statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Descriptive Statistics:\n",
      "                gdp       edu_m       edu_f         edu       gra_m  \\\n",
      "count    220.000000  220.000000  220.000000  220.000000  220.000000   \n",
      "mean   28189.163922   28.137597   26.365059   27.251328   30.411045   \n",
      "std    24418.520746   14.491159   13.999286   13.411303   14.293321   \n",
      "min      892.382103    4.890618    4.443403    4.667010    5.002174   \n",
      "25%     3720.652241   14.562961   11.889083   13.025368   16.560459   \n",
      "50%    27749.813115   30.689915   29.336174   31.556111   31.574318   \n",
      "75%    48413.461503   36.990921   38.090203   37.474835   43.341550   \n",
      "max    79434.625650   98.777816   57.976088   63.973501   53.613346   \n",
      "\n",
      "            gra_f         gee        er_m        er_f          er         law  \\\n",
      "count  220.000000  220.000000  220.000000  220.000000  220.000000  220.000000   \n",
      "mean    42.277963    4.973956   69.755209   52.008773   60.910727    0.390909   \n",
      "std     21.187207    1.698149    6.245580   10.678652    6.158613    0.489067   \n",
      "min      5.209082    2.416861   56.215000   24.194000   46.485000    0.000000   \n",
      "25%     22.342985    3.471106   64.492250   48.652750   57.874250    0.000000   \n",
      "50%     43.434549    4.502880   70.052500   52.391000   60.767500    0.000000   \n",
      "75%     62.207563    6.448677   74.941500   58.370250   63.458250    1.000000   \n",
      "max     80.201691    8.559550   80.583000   71.883000   76.069000    1.000000   \n",
      "\n",
      "              cpi         gpi  \n",
      "count  220.000000  220.000000  \n",
      "mean     3.121745    0.934221  \n",
      "std      3.161698    0.233189  \n",
      "min     -1.352837    0.295301  \n",
      "25%      1.036921    0.786062  \n",
      "50%      2.320137    0.910528  \n",
      "75%      3.960611    1.155516  \n",
      "max     23.115448    1.304352  \n"
     ]
    }
   ],
   "source": [
    "# Descriptive statistics\n",
    "print(\"Descriptive Statistics:\")\n",
    "print(data[['gdp', 'edu_m', 'edu_f', 'edu', 'gra_m', 'gra_f', 'gee', 'er_m', 'er_f', 'er', 'law', 'cpi', 'gpi']].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (2)Correlation analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Correlation Matrix:\n",
      "            gdp     edu_m     edu_f       edu     gra_m     gra_f       gee  \\\n",
      "gdp    1.000000  0.520934  0.857139  0.728798  0.605101  0.857713  0.758551   \n",
      "edu_m  0.520934  1.000000  0.772625  0.943508  0.607414  0.527537  0.274316   \n",
      "edu_f  0.857139  0.772625  1.000000  0.939339  0.666159  0.823154  0.623005   \n",
      "edu    0.728798  0.943508  0.939339  1.000000  0.675843  0.714628  0.473361   \n",
      "gra_m  0.605101  0.607414  0.666159  0.675843  1.000000  0.796967  0.262682   \n",
      "gra_f  0.857713  0.527537  0.823154  0.714628  0.796967  1.000000  0.660332   \n",
      "gee    0.758551  0.274316  0.623005  0.473361  0.262682  0.660332  1.000000   \n",
      "er_m  -0.761743 -0.333502 -0.726076 -0.559132 -0.624204 -0.850193 -0.713710   \n",
      "er_f   0.278167  0.319970  0.307443  0.333328 -0.084405  0.068789  0.200868   \n",
      "er    -0.161470  0.087962 -0.121602 -0.015944 -0.406046 -0.382857 -0.191715   \n",
      "law    0.700248  0.413708  0.635889  0.555393  0.232805  0.618495  0.671724   \n",
      "cpi   -0.398780 -0.376727 -0.461556 -0.444426 -0.318820 -0.391272 -0.266898   \n",
      "gpi    0.668466  0.023284  0.595284  0.323271  0.178630  0.588434  0.638416   \n",
      "\n",
      "           er_m      er_f        er       law       cpi       gpi  \n",
      "gdp   -0.761743  0.278167 -0.161470  0.700248 -0.398780  0.668466  \n",
      "edu_m -0.333502  0.319970  0.087962  0.413708 -0.376727  0.023284  \n",
      "edu_f -0.726076  0.307443 -0.121602  0.635889 -0.461556  0.595284  \n",
      "edu   -0.559132  0.333328 -0.015944  0.555393 -0.444426  0.323271  \n",
      "gra_m -0.624204 -0.084405 -0.406046  0.232805 -0.318820  0.178630  \n",
      "gra_f -0.850193  0.068789 -0.382857  0.618495 -0.391272  0.588434  \n",
      "gee   -0.713710  0.200868 -0.191715  0.671724 -0.266898  0.638416  \n",
      "er_m   1.000000  0.032558  0.543057 -0.642262  0.411012 -0.662462  \n",
      "er_f   0.032558  1.000000  0.856440  0.401037 -0.112867  0.280467  \n",
      "er     0.543057  0.856440  1.000000  0.011169  0.122418 -0.104111  \n",
      "law   -0.642262  0.401037  0.011169  1.000000 -0.243035  0.550714  \n",
      "cpi    0.411012 -0.112867  0.122418 -0.243035  1.000000 -0.353846  \n",
      "gpi   -0.662462  0.280467 -0.104111  0.550714 -0.353846  1.000000  \n"
     ]
    },
    {
     "data": {
      "image/png": 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8FA8PD6Vbt27K0aNHba4u9OWXXyoRERGKRqNRAGXu3LmKohRenUlR8ldYev3115WwsDBFp9MpwcHByrPPPqskJydbxYWFhSk9e/YstD9t27ZV2rZta3Nfr8c/qzPdjK0Vlo4fP6507txZ8fDwUHx8fJSBAwcqFy9etLkC0bhx45SQkBBFrVZbfX5F5X7ttWufX0ZGhlKtWjWlRo0aSmZmplXcc889p+h0OmXXrl1272dRK2cV55jIzc1VxowZo4SGhirOzs5KgwYNlOXLl9v8P/3zzz+V+vXrK3q9XgEs+3dtdab4+Hir+GHDhilubm6F8rV1jH/00UdKeHi4otfrlerVqyuzZ8+2bNfW5zB9+nSlYsWKik6nU6pVq6b89NNPN/v4hBDiP0ulKIpSKq0XIYQQ4h6hUql47rnn+Prrr0s7FSGEuC/InAghhBBCCCFEiUgjQgghhBBCCFEiMrFaCCHEf56M7BVCiJKRngghhBBCCCHukq1bt/LAAw8QEhKCSqVi+fLlt6yzZcsWGjZsiLOzMxUqVGDGjBmFYpYsWUKNGjXQ6/XUqFGDZcuW3YHsC0gjQgghhBBCiLskMzOTunXrFnshh/Pnz9OjRw9at27NgQMHePPNN3nxxRdZsmSJJWbHjh0MHjyYRx99lEOHDvHoo48yaNAgdu3adad2A1mdSQghhBBCiFKgUqlYtmwZffv2LTLm9ddfZ+XKlURGRlrKRo4cyaFDhyw38Bw8eDBpaWn88ccflphu3brh4+PDL7/8ckdyl54IIYQQQggh7JSbm0taWprVIzc312Hb37FjR6GbnXbt2pW9e/diMBhuGrN9+3aH5XEjmVgthBBCCCHue7/rqpbK++556yEmTZpkVTZhwgQmTpzokO3HxsZSpkwZq7IyZcpgNBpJSEggODi4yJjY2FiH5GDLPdWIKK3/fEfoaThJ1ncTSjsNu7g+OYmDXVqXdhp2q7duG5efH1jaadit7NeLSP30hdJOwy5eY6fe98dO1FN9SjsNu4V/u4IeTx4p7TTssvq72mTs+q2007Cbe9MHWHMwr7TTsFu3ek7EvPpwaadhl+DPfuZ/S82lnYbdxvRXE/fW46Wdht0C359X2incc8aNG8fo0aOtyvR6vUPfQ6VSWT2/Nhvh+nJbMTeWOdI91YgQQgghhBDifqLX6x3eaLheUFBQoR6FuLg4tFotfn5+N425sXfCkWROhBBCCCGEuO+pdKpSedxpzZs3Z/369VZl69ato1GjRuh0upvGtGjR4o7lJT0RQgghhBBC3CUZGRmcOXPG8vz8+fMcPHgQX19fypcvz7hx44iOjmb+/PlA/kpMX3/9NaNHj+bpp59mx44dzJkzx2rVpZdeeok2bdrw8ccf06dPH1asWMGff/7JX3/9dcf2QxoRQgghhBDivqfW3vleAUfYu3cv7du3tzy/Np9i2LBhzJs3j5iYGC5evGh5PSIigtWrV/PKK68wbdo0QkJCmDJlCg8++KAlpkWLFvz666+8/fbbjB8/nooVK7JgwQKaNm16x/ZDGhFCCCGEEELcJe3ateNmt2mbN29eobK2bduyf//+m253wIABDBgw4HbTKzZpRAghhBBCiPueSidTfe8m+bSFEEIIIYQQJSKNCCGEEEIIIUSJyHAmIYQQQghx37tfJlb/W0hPhBBCCCGEEKJEpCdCCCGEEELc9+7Gjd9EAbsbESdPnmTq1KlERkaiUqmoVq0aL7zwAlWrVnVkfkIIIYQQQoh7jF3DmRYvXkytWrXYt28fdevWpU6dOuzfv59atWqxaNEiR+cohBBCCCGEuIfY1RPx2muvMW7cOCZPnmxVPmHCBF5//XUGDhzokOSEEEIIIYQoDplYfXfZ1RMRGxvLY489Vqh86NChxMbG3nZSQgghhBBCiHuXXT0R7dq1Y9u2bVSqVMmq/K+//qJ169YOSUwIIYQQQojikonVd5ddjYjevXvz+uuvs2/fPpo1awbAzp07WbRoEZMmTWLlypVWsaXFt1UjKrw6HK8GtXAOCWTvg6O4unJDqeVzzcL9p/l+9wkSMrKp6O/FmI71aVAu0Gbs3otXefqXTYXKlz7Vgwg/z/x/HzzLqmPnOROfCkD1IF9eaFOHWiF+Ds/d74G+BA58CJ2vHzkXooj+ZgqZRw8XGe/ToTOBAx9GH1oWU2YmaXt3cWXWNEzpaZYYjZs7QU88jXfLtmg83MmLjSF65jTS9+x0eP5urbvg0bEPGi9vDDGXSVkyl7yzJ2znPvQ53Jq1K1RuiLnE1fdHA+BctwmeXfuj9Q8CjQZjfCwZG34ja89Wh+cO4FSvNfrGHVG5e2JOiCF741JM0WeLrqDRom/eDacajVG5eWDOSCF3xzoMR/M/W7VfEM6teqIpUw61lx/ZG5eQt2/zHckd7u/jx6Nddzy79kPr7UPelYsk/TqH3NPHbcb6P/Ei7i07FirPi77IlQkvWJ6rXdzw7jcU1wbN0Li5Y0i4SvLCuWQf2efQ3K95pE8g3dr64u6q4eS5LKb/eIWLV3KLjP/otQjqVHMvVL77UBoTv7pgee7nreWJgUE0qu2Bk05N9NVcvpp7mTMXchyW+8I//+aH1ZtJSE2nQmgZxjzSh/pVK9iM3bjnCIs3bufkxSsYDEYqhAbxTL8utKhjvfDHz2u2snjjDmITk/H2cKNj4zo8P7AHeiedw/K+RlEU1iz+hu0bFpOdkUZY5doMePItgstVKrJOzKUzrF44jcvnj5MUf4V+j71Gu56PFopLSbrKyp++IPLgXxjycgkMDuOhkZMoV6Gmw/cDwLVFJ9za9ULj6Y0xNprUFfMxnD9pM9ZryAhcG7ctVG6IvUzCp6/dkfxuRVEU9m+YxondC8nNTiOwXB1a9BmPb5nKxap/9tDvbPx1DGE1OtLl0a/vaK4uTTvg2qo7ag9vjHHRZPz+M4YLp4quoNHi1qEPznWbo/bwwpyaTOaW38jZtw0A7+Fv4FShWqFquScPkTr/izu1G+Jfyq5GxKhRowCYPn0606dPt/kagEqlwmQy3UZ6t0fj5kra4ZNc/n4pDRfd2S96ca2NvMinGw4wrktD6oX6s+TgWZ5ftJUlT3Un2NOtyHrLn+6B23V/2Hxc9ZZ/770UR7fqYdTt5I+TVsP3uyJ5duFmlgzvTqCHq8Ny927bgdCRL3J56udkHjuCf8/eVHj/U0489SiG+LhC8W41a1N+7FtEz5xK2s7t6Pz8KfvSGMqNfp2oSW8BoNJqqfjR5xhSUoh6dzx5CXE4BQRiys5yWN7XuDRogfeDT5C8YDZ5507i1qoz/qPe4up7r2BKTigUn7J4LqkrfrI8V2nUBI77H9kHdljKlKwM0tYsxXg1GsVkxKVWQ3yGjsKUkUpu5CGH5q+r2gDnDv3JXr8QU/Q5nOq2xG3As6R/9z5KerLNOq4PPIHKzYOstT9jTo5H7eoB6oJRjCqdE+aUBAwnD+Dcvr9D873R/Xz8uDZuhe+Q4ST+NJPcM5F4tOlKmZfeIfqd5zElFT52En/9luQl8wsKNBpCJnxJ1r6/ryvTUmb0JEzpqcTP+BhjUiJaX3/MOdkOzf2aAd396dfFn8/nXCb6ai5DegXy/pgInnnzFNk5Zpt13pt2EZ2m4Mqeh7uGaZMq89feVEuZu6ua/71ZkcMnMnjniyhS0owEBzqRkWV7m/ZYt/Mgn/20kjeG9ade5XCWbNrJC//7lkUfjiXY36dQ/P6T52haqwrPDeyOh6sLK7ft4ZUvvuP7CS9SLTwUgNXb9zN10WreGT6IupXDuRAbz8TZCwB49ZE+Dsv9mg0rv2PT7/N55Nn3CAgOY93SWUx//xne+uI3nF1s//bn5ebgX6Ys9Zt1Ydn8T2zGZGWk8tU7j1GpRmNGjvsGd09fEq5ewsXV0+H7AOBcrxmefR4jdel3GM6fwrV5R3yffp34T8ZiTkksFJ+2fD7pv/9aUKDWEPDqh+Qc2nVH8iuOQ1u/5chf82g74AO8/MM5sGkGf8wZzsBX/8BJX/TfYYD05Gh2rf6UoPCGdzxPfe0muPd4mPTf5mO4cBqXxu3xGjaapK/exJyaZLOO10OjULt5kbbsO0yJcajdPUCtsbye+vNUVJqCUz+Vqxu+z79L7pE9d3x/7gaZE3F32TUnwmw2F+tRmg0IgPi1Wzk14Util68v1Tyu9+OeE/StU4H+dStSwd+LsZ0aEOThyqIDZ25az9fVGX93F8tDc92J4AcPNGdQg8pULeNDhJ8n47s1RlEUdl246tDcAx4cTNKa30las4rcSxeInjEVQ3wc/g/0sxnvWr0meVdjSVi+hLzYGDKPHSHx95W4Vim4GujbtScaD0/OTxxH5vEjGOKuknnsCDnnbnJ13U4eHXqRuWMjWTs2YrwaTeqSeZiSE3Br3cVmvJKThTk9xfLQla+I2sWNzB0FPUO5p4+Tc3g3xqvRmBKukrF5NYYrF9DbuNJzu5watSfvyA4MR3ZgTrpKzqalmNOTcarXyma8Nrw62nKVyFoyA9OFkyhpSZhiL2C6ct4SY4q9SM6WFRhO7AeT0eE5X+9+Pn68Ovch/a8/ydi2HkPMZZIWzMGYnIBHu+4245XsLExpKZaHPqwSald30v8q6An1aNUJtZs7cdM+IPfMCUxJ8eSeicRwOcqhuV/Tt7M/v66KY/v+NC5E5/LZnMvondS0a+pdZJ2MTBPJaUbLo35Nd3LzzGzbU9CIGNAjgPgkA198F82p89nEJRo4FJlJbHyew3L/cc0W+rRtQr92TYkILcOYoX0o4+vN4o07bMaPGdqHYT3bU7NCecoHBfD8wB6UD/Jn68FjlpgjZ6KoWzmc7i0aEBLgS/PaVenarB7Hz192WN7XKIrCltU/0qXf09Rt2omQ8pUZ+tz7GHJz2PfX70XWC6tUiz5DX6VBy+5odU42Y/5c+R3efkE8Muo9wirVxi8wlKq1m+EfVM7h+wHg1qYHWbs3k71rM8a4K6St+AFzSiJuLTrZjFdysjGnp1oeunIVULm4kb1nyx3J71YUReHo3/Op134EEbW64BtUhXYDP8JoyOHswVU3rWs2m9i04DUadHoeD9878/lez7VlV7L3bSVn71ZM8TFkrP4Zc2oSLk072Ix3qlwbXXg1UuZ/juHsccwpCRgvn8d4seD8QsnOxJyRank4VaqFYsgj5+juO74/4t9H7lh9FxlMJiJjk2keEWRV3iwiiEPRha9mXm/IvLV0/no5I37dyJ5bNA5yDCaMZgUvZ9t/dOyh0mpxrVyF9P3WPzTp+/bgVqOWzTqZx4+i8w/Ao3H+kDettw/erduRtqvgD79X85ZkRh6j7AujqblgBVVnfU/gkEetrpY7hEaLrlwFcm7oHciJPIw+onj3NnFr3oHck0ds9lpco69SC21gCLlnI28r3ULUGjRB5TBGWQ+9MkadQBsaYbOKtlJtTFcv4dSkEx4j38V9+Hic2/UFreOHatzKfX38aLQ4hVUk59hBq+KcYwdxrli8xqJ7607kRB7ClBRvKXOp15jccyfxe3gE5T7/npBJU/DqMQBUjv9ZDgrQ4eutY/+xDEuZ0ahw5GQm1SsVv7eya2tftuxOJTdPsZQ1q+fJ6ahsxj1bnp+/rM7UCZXo2qZw74C9DEYjJ6KiaVarilV5s9pVOHw6qljbMJvNZGbn4uVWsK/1qkQQGXWZo2cvAnA5LpG/D52gVd3qDsv9msS4y6SlJFCtTgtLmVbnRMUaDTl/6vZ6LI/u3Uy5CjWY+/lo3nq6LZ+8PpDtGxbfbsq2aTToykaQe9J6CGLuySPowqsUUcmaa5N25J0+etPf0TspPfky2ekJlK3c0lKm0ToRHNGYqxcO3LTugQ3TcXbzoVrjAXc6TdBo0IaEk3fmqFVx3pmj6MrbHgLnVL0exujzuLbugd/rX+D7yke4dxt80998l4atyT2yCwyOa/SL/45iD2eaMmVKsTf64osv2pXMv11yVh4mRcHX1dmq3M9NT2Km7bHD/m4ujO/amOpBPuSZzPx+LIoRv25i9sMdaFjEPIopWw4R6O5C0/Agm6/bQ+PphUqjxZBsPWzGkJyMh4+vzTpZx49y4eN3CX9rEmonJ1RaLanbt3F52peWGKfgENzrBZG8cT3n3h6LPrQcZZ9/BZVGw9Wf5jksf7W7ByqNBnN6ilW5OT0Ftaf3ret7euNcoz5J874q9JrK2ZXg92ei0mrBbCZ5wbfknih6nL89VC5uqNQalMx0q3IlMx2Vm+1hC2pvPzShFVCMBrKWf4vKxQ2XzoNQObuSveZnh+Z3K/fz8aNx90Sl0WBKS7EqN6WloPG69cmyxssHl1oNiZ/9mVW5zj8IbbVAMnZu4epXk9EFhuD7yDOg1pC6aoFDcr/GxzP/JCIlzbq3KSXNSKBf8RqVVSJcCC/rzJdzra/UBwU40bO9L8vWJrDg9ziqRrgy8uEQDEaFjdtTbjv3lPRMTGYzfl4eVuV+nh4kpqYXUcvaj39sISc3j85N61rKujarT3JaJsPfm4aCgslkZkCH5jzxgO2rvLcj/Z9hPh5e1vPUPLz8SI6Pua1tJ8Zd5u/1C2nX8zE693uaC2eOsHTuR2i1TjRp69g5iWq3f35HM1Ktyk0Zqeg9vG5d38MbfbW6pPxUesOLs9PzGy8u7v5W5S7ufqSnXCmyXmzUfk7uXUL/F5fd0fyuUbte+6zTrMrNGWmo3W1/1hqfQHRhVVCMBlJ/moLa1QOP3o+hcnUjfel3heK1ZSPQBpUjbVnh1+5XKo0MZ7qbit2I+OIL6wk38fHxZGVl4e3tDUBKSgqurq4EBgbeshGRm5tLbq71ZD69Xl9E9L+P6oZjXFGgqMM+3M+TcL+Ck8S6of5cTcti/u4TNhsR83ZFsibyIrMf6oBeqyn0+m1TFKun+fui2AzVlw+n7KiXiP1pHul7d6Pz9SPk6VGUe2kMlz7/+J8NqDGmpHDpy0/BbCb79Cl0fv4EDnjIoY2IIqlUhfbJFrdm7TBnZ5J9uPC4USU3m6sfjkWtd0ZftRbe/YdhSrxa5KTb23NDrqrCRQWv5e9b1qrvIS+/kZq9aRmufZ4k+89FYDTcgfxu4X4+fm48Top57Li36IA5K5OsAzeMAVepMKWlkjh/Oihm8i6cRePtg2fXfrfdiGjXzJsXHguxPJ/w5QXbu2CjrChdWvsQdTmHU+et52yoVHA6Kpvvl+b3kJ67mEP5UD092/k5pBFxfa7XU7jJD+d11uw4wMxl6/j85Sfw9SxoiOyNPMN3v23gjWH9qVWxPJeuJvC/H1fgv3w9T/ftfFu57t22igWzC+6jNOKNaf/sxI0//jbKSkgxmylXsSYPPPQSAGUjqhN7+Sx/r1/g8EZEwZtaPy3uHrg0boOSk0XO0b0OT6koZw78xrblEy3Puw37BrB9PKmK2JO83Ew2LXyN1v0n4+zmuF62YrH1u1MElSr/D0Lawpkoufnf04zVv+D50HOkr/yh0G++S8M2GGMvYbx83sbWhLi1Yjcizp8vOMh+/vlnpk+fzpw5c6haNX8oyMmTJ3n66acZMWLELbf14YcfMmnSJKuyCRMm0Li4ydynfFyd0KhUhXodkrJy8XVzLqJWYbVD/Fh97EKh8vm7TjBnx3FmDG5PlUDv203XiiktFcVkROdrfdVY6+2D8Yary9eUGTKUzGNHiF/0CwA5589yOSebyl9MJ2betxiTEjEmJaIYjWAumISZczEKnZ8fKq02/zUHMGeko5hMqD28rcrV7l6Y01NtV7qOa7MOZO3eanvegKJgSojFBBiio9AFlcWjSz+HNiKU7EwUs6lQr4PK1QMlK812nYy0/CuGeQXHmzkxFpVKjdrdG3NKvM16d8L9fPyYMtJQTKZCvQ4aD69CvRO2uLfqRMbOzYWOHVNqMorJBEpB7oaYy2i9fUGjva05KrsOpnHyXMHkct0/kw19vLQkpxZs18tTW6h3wha9k4q2Tbz5cXnhoZTJKUYu3bDC06UrubRseOsr08Xh7eGGRq0m4YZeh6S0DPw8PYqolW/dzoNMnrOQj59/lKY3DIf6ZslaerRoQL92TQGoXC6YnNw83pu7mOG9O6K+jSFxtRq1J6xyHctz4z9DRdJTEvDyCbCUp6clFuqdKClPnwCCQitalZUJrcChXX/e1nZtMWde+x21/r9Vu3thKs7vaJN2ZO3dBndxvmT5Gh3oX67g/8Jkyv+/yMpIwNWz4EJcTkYSLu62/y/SEy+SkRzN2vkFC8co/3xvv32rFoNGr8bTr7xD8zZnFfFZu3kU6gm6xpSegjot2dKAADDGX0GlVqPx8sWUeN33V+eEvk5TMv+8Oz0rd4taeiLuKrt+JcePH8/UqVMtDQiAqlWr8sUXX/D222/fsv64ceNITU21eowbN86eVO4rOo2G6kE+7IyyviHfzqhY6ob6F1GrsBNXk/F3t250fL8rktnbjzFtYFtqBtseHnI7FKORrNOn8Ghg3dTzaNCYzONHbdZR6/UoZusVWm58nnnsCPqQUKurK/rQchgSExzWgADAZMRw6RzO1epYFTtXq0NuEUsTWvKpXANdYDCZOzYW881UqBw978BswhR7CW2Y9Rh8bVhVjNG2ryIZo8/ld3tfNyFT7RuIYjZjzkhxbH63cF8fPyYjeRfO4lyjrlWxc4165BSxPLAlpmotdGVCyNhWeHGHnDOR6AKDrHLXlgnBmJJ025Pcs3PMxMTlWR4Xr+SSlGKgQY2C5Vq1GhW1q7oReebWK1m1buyFTqdi446UQq8dP5NFaJB1T3JokJ64RMeMsdZptVQLD2XXUetlLXcdPUWdyuFF1luz4wATZ//K+88+Qut6NQq9npOXh1ptfcKhVqtBUYrs3CsuZxc3AoLKWx5BZSvi6e3PycMF83mMRgNnj+8jokrdm2zp1iKq1iMuJsqqLC4mCp+A4Nvark0mE4bL59FXqW1V7FSlFoaomyw7CjhVrI42IIjs3Zsdn9fN3lfvhpd/mOXhE1gJFw9/ok9vt8SYjHnEnN9DmbD6NrfhFVCBB19aQf8XlloeYdU7EFKhKf1fWIqbl+OGDhckZcJ4JQqnStbL9DpVqonhou2FWAwXT6Px8EblVPB91PgHoZjNmG5Yzcm5VhNUGh05B7ffuBkhis2uRkRMTAwGQ+GhECaTiatXb70ikF6vx9PT0+pxJ4Yzadxc8axbDc+6+SderhFl8axbDedyd+DHtZiGNq7GskPnWH74HOcSUvnfhv3EpmUxoF7+RKkpWw7x9qqC9e1/2nOSTacucyEpnbPxqUzZcogNpy4zuEHBetbzdkUybdsRJvRoQoiXGwkZ2SRkZJOV59jhKvFLFuDbrRe+XXugLxdGyMgX0AUGkrBqOQDBT46g/Ni3LPFpO7fj3aotfr364hQUjFuN2pQd9RKZJ45jTMofI5ywajkaTy9Cn30JfWg5PJs0p8xDj5KwcqlDcwdI37gKtxYdcW3WHm2ZULz6D0Pj60/mtnUAePZ+GJ9Hny9Uz7V5R3LPn8IYc6nQax5d+qKvVgeNXyDaMiG4d+iFa9M2ZO52/H0i8vZuwqlOc3S1mqH2LYNz+/6oPX3JO/QXAPrWD+DSo2ANeUPkXpTsTFy7D0XtF4SmbEWc2/bFcGRnQbe2WoM6MBR1YChotKjdvfKfexe/UVtc9/Pxk7p+BR6tO+PesiO64LL4DB6O1tef9M1rAPDu/yj+T75cqJ57q07knj2J4crFQq+lb16D2t0T3yFPoS0Tgkvthnj3HEj6ptUOzf2a5esTGNQrkOYNPAkL1TN6eFly88xs3pViiXn1qbI8/mCZQnW7tPZlx/400jMLX0Feti6BahVcGdQzgOBAJ9o19aJ7W19WbbS9BKU9hnZry/Itu1mxZTfno6/y2U8riE1MYUCH/En3Uxeu5p2Zv1ji1+w4wDuzfuHlhx6gdsXyJKSkkZCSRnpWwRXaNvVqsHjDDtbuPEB0fCI7j57imyVraFO/ptXqd46gUqlo22Mo65d/y6HdG7hy8TQ/TX8Lnd6Zhq16WuJ+/PpNfvv5S8tzo9HA5agTXI46gdFoIDU5jstRJ4iPLTie2vV4jKjTh1m3bDbxsRfZ+9fv7NiwhNZdhjh0H67J3Loa16btcWnSFm1gCB69h6Lx8SdrR/7KYx49BuP10LOF6rk0aUfehdMYYx2/+lVJqFQqarV8jIObZ3H+2HqSYk+xZfGbaHXOVKzXyxK3aeHr7F7zOQBanR7foCpWDydnD3R6N3yDqqDROm4Rk+tl/b0Wl4ZtcW7YGk1AMO49Hsq/n8/u/BUC3boMwGPA05b43EM7MWdl4NH/KTQBIejCq+DebXD+PSJuGMrk3Kg1uZH7UbIz70ju4r/BrvtEdOzYkaeffpo5c+bQsGFDVCoVe/fuZcSIEXTqZHuZt9Lg1bAWzTf8YHle439vAnBp/lIODy+dno+u1cuTmp3LrL+PkpCZQyV/L6YObEOIV/7a1AkZ2cSmFXypDWYzX2w6SFxGNnqthor+nkwZ0IbWFQvGOy/cfxqDyczY5X9bvdeIljUZ2cr6itHtSNmyEY2nJ0GPPI7W14+cC+c59/ZrGOLyG446Xz+cAgtOQJLW/4Ha1RX/3v0JfeY5TJkZpB/cT8y33xTsX3wcZ8eNJnTkC1SdORdDQgLxyxYTt/CnQu9/u7L3byfFzR3P7gPQePpgiLlEwvQPLKuEaDx90PpanzyrnF1xqdeU1MVzbW5T5eSM96Cn0Hr7oRjyMFyNJun7qWTvd/zVHcPJ/ahc3HBu0Q2VW/7N5jKXfIOSlj8cSO3uhdrjuiE3hjwyF03DueMA3B8di5KdieHkAXL+KljGUOXuhcewNyzP9U06oW/SCePF02QuKP5iCsVxPx8/WXv+IsnNA+8HBqPx8iXvygWufjXZstqS1ssHrd8Nx46LK64NWpD062yb2zQlJ3D18wn4Dh5O6MSvMCYnkvbnb6T+4fgGNMDiPxLQO6l5bmgI7m75N5t7+7PzVveICPDVcUNnD6FlnKhVxY23/me7x+t0VDbvTbvA4w8G8XDvQGLj85j5yxU270xxWO5dmtUjJSOT2SvWk5CSRsWyQUx5dTjB/vm9rgkpacQmFgyLW7ppByaTmY/nL+Pj+QXDNXq1asSkZ/JProf36YRKpWL64jXEJ6fi7eFOm/o1eG6A7WV7b1fH3k9iyMtl8Zz3yMpMI6xSbZ59c6bVPSKSE2NQXdc7kpoUx6evD7Q83/jbPDb+No9KNRrxwoT836SwSrUY/uqXrPrlS9YumYFfQCj9hr1Go9YFJ8SOlHNwJ2mu7rh37p9/s7mYyyR/+4nld1Tt6Y3G23pYkMrZBZc6TUhdPt/WJu+6um2ewmTI5e8Vk8nLTiOgXB26P/mt1T0iMlNiUN2BldJKIvfIbjJc3XFr3we1h1f+0uTzP7fcj0Pt4Y3muuFwSl4uKXP/h8cDj+A7agLmrAxyj+4hY/0Sq+1q/MrgFF6V5O8+vav7czeo1DKc6W5SKUpxp9UViI+PZ9iwYaxZswadLn/YhsFgoFu3bsydO5cyZQpfySqO33XFW2rzXtTTcJKs7yaUdhp2cX1yEge7tC7tNOxWb902Lj8/8NaB96iyXy8i9dMXbh14D/IaO/W+P3ainnL8jcXulvBvV9DjySOlnYZdVn9Xm4xdv5V2GnZzb/oAaw7ev8tidqvnRMyrD5d2GnYJ/uxn/rfUcTczvNvG9FcT99bjpZ2G3QLfn1faKRTp7/p3/iaAtrQ8sK9U3re02dUTERAQwOrVqzl9+jSRkZEoikL16tWpUqV460QLIYQQQgjhSCqN3P7sbrKrETF69OhCZVu2bEGlUuHs7EylSpXo06cPvr6On+ArhBBCCCGEKF12NSIOHDjA/v37MZlMVK1aFUVROH36NBqNhmrVqjF9+nReffVV/vrrL2rUKLwqhhBCCCGEEI4kS7zeXXb1+/Tp04dOnTpx5coV9u3bx/79+4mOjqZz58489NBDREdH06ZNG1555RVH5yuEEEIIIYQoZXY1Ij799FPeffddPD0Lbnzl6enJxIkT+eSTT3B1deWdd95h377/5kQTIYQQQggh/s3sakSkpqYSFxdXqDw+Pp60tPy753p7e5OXd/+uXCGEEEIIIe4fKrWqVB7/VXYPZ3ryySdZtmwZly9fJjo6mmXLljF8+HD69u0LwO7du2W1JiGEEEIIIf6F7JpYPXPmTF555RWGDBmC0WjM35BWy7Bhw/jiiy8AqFatGt9++63jMhVCCCGEEKIIMrH67rKrEeHu7s7s2bP54osvOHfuHIqiULFiRdzd3S0x9erVc1SOQgghhBBCiHuIXY2Ia9zd3alTp46jchFCCCGEEELcB26rESGEEEIIIcS9QCXDme4quT+4EEIIIYQQokSkJ0IIIYQQQtz3VGq5Nn43yacthBBCCCGEKBHpiRBCCCGEEPe9//KN30qD9EQIIYQQQgghSkQaEUIIIYQQQogSkeFMQgghhBDivid3rL67pCdCCCGEEEIIUSIqRVGU0k5CCCGEEEKI23GoW5tSed+6a7aWyvuWtntqOFPWdxNKOwW7uT45id91VUs7Dbv0NJzk3OO9SjsNu1WYt4pNleuWdhp2a3/6EKmfvlDaadjFa+xUsua8U9pp2M11+GTSd/9e2mnYzaNJT2asLe0s7DOyK2RtW1TaadjNtfVAzp89U9pp2C2iYiVyVn1T2mnYxbnXs3y58v69/vlybxXHz1wp7TTsVqNSSGmnIO4RMpxJCCGEEEIIUSJ290RER0fz999/ExcXh9lstnrtxRdfvO3EhBBCCCGEKC65Y/XdZVcjYu7cuYwcORInJyf8/PxQqQpmw6tUKmlECCGEEEII8S9mVyPinXfe4Z133mHcuHGopdUnhBBCCCFKmdyx+u6yqwWQlZXFkCFDpAEhhBBCCCHEf5BdrYDhw4ezaNH9u6qGEEIIIYT4d1FrVKXy+K+yazjThx9+SK9evVizZg21a9dGp9NZvf755587JDkhhBBCCCHEvceuRsQHH3zA2rVrqVo1/74IN06sFkIIIYQQQvx72dWI+Pzzz/nuu+94/PHHHZyOEEIIIYQQJScTq+8uu+ZE6PV6WrZs6ehchBBCCCGEEPcBuxoRL730ElOnTnV0LkIIIYQQQthFpVaXyuO/yq7hTLt372bjxo2sWrWKmjVrFppYvXTpUockJ4QQQgghhLj32NWI8Pb2pn///o7ORQghhBBCCHEfsKsRMXfu3GLF/f333zRq1Ai9Xm/P2wghhBBCCFEsMrH67rqjA7m6d+9OdHT0nXwLIYQQQgghxF1mV09EcSmKcke2u3D/ab7ffYKEjGwq+nsxpmN9GpQLtBm79+JVnv5lU6HypU/1IMLPM//fB8+y6th5zsSnAlA9yJcX2tShVojfHcm/uHxbNaLCq8PxalAL55BA9j44iqsrN5RqTp4deuDVvT8ab18M0RdJ/Hk2OaeO2YwNeOplPFp1KlSeF32By289B0DwGx/iUq12oZisQ3uI/WKSY5MHQh4eRPmnHscp0J+s02c5/f4npO49UGR86CODCX10CM6hIeReiSXqm9lcXb7KZmxgz27U/PJj4tdv5OioVxyeO4BTvdboG3dE5e6JOSGG7I1LMUWfLbqCRou+eTecajRG5eaBOSOF3B3rMBzdCYDaLwjnVj3RlCmH2suP7I1LyNu3+Y7kDrDwwGm+332y4LvboT4NygXYjN17MY6nf7Xx3R3e3fLd3XDqMnN2HOdSSgZGs5nyPh482rgqvWqGOzz3RX/+zQ+/byIhNY0KoUG8OrQv9atWsBm7cc9hFm/YzqmL0RgMRiqUDeKZfl1pXqeaJeaZ96ex/0Th/7uWdavz1ZinHZ6/oijs/ONrjmxfQE52GsFhdWk/8B38gysXWef0oXXsXjeD1ISLmExGfALCaND+CWo06WuJObTtZw7//QtpifkXjPyCK9O02ygiarR1aP4LN+3i+7XbSEjJoGJIIGOG9KBBlXCbsQdOR/HV4nVExcaTk2cg2M+bB9s0ZmiXglUFl27dw6odBzkTfRWA6mEhvNCvC7UqlL3tXH9btYrFS5aSlJREWFh5Rj7zDLVq1Soy/vCRI8yaPZsLFy7i5+fLwAcH0LNnD6uYZcuXs+r31cTHx+Pp6UnrVi154vHHcXJyAsBkMvHDjz+xafNmkpOT8fX1oXOnTjw0ZAjq25z4ueDvQ8zbvI+EtEwqBvnxWp+2NKgQest6B85fYfj0RVQK8mPhq0Mt5WdiE5m+ZgeRl69yJTmdsX3aMLRNg9vK8WYURWHv+q85vmshuVlplClfh9b93sE3qOhj/9yRdezfOJPUhIuYTUa8/MOo2/YJqjbsY4kxm4zsWf81p/f/RlZ6Aq6eAVRr1I+GHZ+1e7LtH6uWs3zpApKTEilXPpzhzzxPjVp1iow/euQgc2dP59LFKHx9/ek7YAjdevS2vL5uzSo2b1zHxajzAFSsVIVHhj1FlarVLTFrfl/BmtUribsaC0C5sHAGPfQYDRs1tWsfSpP0RNxdd7QRcSesjbzIpxsOMK5LQ+qF+rPk4FmeX7SVJU91J9jTrch6y5/ugZtTwQRwH9eCIVZ7L8XRrXoYdTv546TV8P2uSJ5duJklw7sT6OF6R/fnZjRurqQdPsnl75fScNHXpZbHNW5NWuP38NMkzP+GnNPH8WzfnaDRE7n05ihMSfGF4hN+mkXSonkFBWoNZd+dSuaevy1FV6e+j0pbcBiq3Twp++5UMvb85fD8A3t0pfJbr3Fq4vuk7j9IyJAB1Pl2Oru79yM3JrZQfMjDA6kw5kVOvjWZtCNH8axTm6rvvYMxLZ3EjVusYvUhwVR8YzQpe/Y5PO9rdFUb4NyhP9nrF2KKPodT3Za4DXiW9O/eR0lPtlnH9YEnULl5kLX2Z8zJ8ahdPeC6P24qnRPmlAQMJw/g3P7OznPK/+4eZFznBtQrG8CSg2d4fvFWlgzvdvPv7lM9cHMqOEau/+56OTvxVPMahPt5olOr2Xb2ChNX78bXVU+LiGCH5b5u5wE++3E5bzz+IHUrR7B003Ze/HQWiz56nSB/n0LxB06epWmtKjw3qAceri78tnU3r3w+h3kTX6JaeP5J6qcvPY7BaLLUSc3I4uG3/kenJnUdlvf19v45m/2b5tJl6Ef4BISza903LJ32BI+/vQYnZ3ebdZxdvWja5Vl8ylRAo9Fx7tgm1v38Jq4efoRXbw2Au3cQrR4Yg3dAeQCO717OytnP8chry27aQCmJtbuP8Omvqxn3yAPUq1SeJVv38PxX81ky+UWC/bwLxbvonRjcoSlVygbhonfiwOkLvPfDClz0TjzYtnH+53HyPN2a1KFuxfI46bR8v2Ybz34xjyWTXyTQx9PuXLds2crMWbN5btQoataozuo/1vD2OxOYNeMbAgMLX+yKjY1l/DsT6N6tG6+NGcOx45FMmz4dLy8vWrXKb/Rs3LSJ7+bOY/TLL1O9RnWio6P57PMvABjxzDMALFy0iNV//MGro18hLCyM06dP8/kXX+Lm6kbfvn0KvW9xrTlwkk9WbOGt/h2oFxHC4h2HGTV7Octee5Tgm3xO6dm5vP3LWppUKkdSRpbVazl5Bsr6edG5bmX+t2JLEVtwnIObv+XQ1nl0GPwhXgHh7P9zBr/NfpKHxv5R5LGvd/WiQYeR+ARWQK3RcSFyM5sWvomLuy/lq+Yf+wc2f8vxHb/SYchH+JSpRPzlo2xa+CZOzh7Uaf1YifP8a+tGvps9jWdGvUy16rVYt+Y33p3wOlO+mUdAYJlC8VdjY3hvwjg6d+vJy2Pe4kTkUWZN/xIvLy+at8xvxB87cpDWbTpQbUQtdE5OLFvyC5PGj2XK9Ln4+edfwPHzD+DRx58mKCS/Ybjpz7V89O7bfDZlFuXDIkq8H+K/475bl+rHPSfoW6cC/etWpIK/F2M7NSDIw5VFB87ctJ6vqzP+7i6Wh+a6E6kPHmjOoAaVqVrGhwg/T8Z3a4yiKOy6cPVO785Nxa/dyqkJXxK7fH2p5nGNV9e+pG9dT/rWdRhiLpP482yMSQl4duhhM17JzsKUmmJ56CMqo3Z1J31bwf6YMzOsYlxq1UPJyyVzt+MbEeWefJSYxcuIWbSMrLPnOfP+p+TGxhL68CCb8UF9enHl18XErV5LzqVo4n5fQ8ziZZR/+gnrQLWaGp99SNRX35B96bLD877GqVF78o7swHBkB+akq+RsWoo5PRmneq1sxmvDq6MtV4msJTMwXTiJkpaEKfYCpivnLTGm2IvkbFmB4cR+MBnvWO4AP+49Sd86EfnfXT9PxnZsQJCHC4sO3KQnBfB11Rf53W1UPpAOVcpSwc+Tcj7uPNyoCpUDvDhwOcGhuf/0xxb6tG1K33bNiAgtw6tD+1HGz5vFG/62Gf/q0H4M69WBmhXKUz4ogOcG9aR8kD/bDhT02nm5u+Hv7Wl57Dp6Emcn3R1pRCiKwv4t82nSZSSV63bBP6QKXR/5GKMhhxP7bPesAZSr3JRKdTvjF1QR74DyNGg3jICQqlw5V9BYrli7AxE12+ITGIFPYAQte72CTu9KbNRBh+X/4/q/6duqIf3bNKJCSCBjh/QkyMeLRZt324yvVj6E7k3rUjG0DCH+PvRsXo8WNStz4HSUJeaDpwcxqH1TqpYPJiI4gPHD+ub/7kfe/Hi8laXLltG1Sxe6d+tK+fLlGTniGQIC/Fn1+2qb8b+vXk1gYAAjRzxD+fLl6d6tK106d2bxdascRkaeoGaNGrRv346gMmVo2KAB7dq25dTpM1YxzZo1pWmTJgSVKUPrVq1oUL8+p06fvq39+WHrfvo1qUn/ZrWoUMaX1/q2I8jbnYXbD9+03ruLN9C9flXqhhduzNcqH8ToB1rTvX5VnLSa28rvVhRF4fC2+TTsOJIKtbvgF1SFDkM+wpiXw+kDRR/7oRWbUqF2Z3zKVMTLvzx1Wj+GX3BVYs/vt8RcvXCA8JodCaveDk/fslSs042ylVsSf/moXbmuXLaIjl160LlrT8qVD2P4M8/j5x/ImtUrbcavXb0S/4BAhj/zPOXKh9G5a086dO7O8qULLTGvjH2b7r36ElGxEmXLlWfUC2NQzAqHDxXsR+OmLWjYuBmhoeUIDS3H0GFP4ezswqkTx+3aD/HfcV81IgwmE5GxyTSPCLIqbxYRxKHom580DJm3ls5fL2fErxvZc4vGQY7BhNGs4OXsdNs5/2totOjDK5F11HroT/bRAzhXqlZEJWsebbqQffwgxsTCvRbXeLbuQsaurSh5ubeV7o1UOi3uNauT9NcOq/Kkv3bg1cD2SZvayQlzbp5VmTknF886tax6T8KfH4EhKZmYxcscmrN1Mho0QeUwRp2wKjZGnUAbavtKkbZSbUxXL+HUpBMeI9/Fffh4nNv1Ba3OZvydZPnuhtvx3f1+HZ2nrWDEr5tu+t291vCPSk6nYRFDpOxhMBo5EXWZZrWrWJU3q1WVw9edlN6M2WwmMycXT/eiezZXbNlFl2b1cXF2/EIUqYmXyUqLJ6xaQYNTq3MitGJjrpwvejjf9RRF4eLJHSTFnSe0YmObMWaziZP7fseYm0VweH2H5G4wGom8cIXmNStZlTerWYlDZy8WaxsnLl7h0NmLNKhS9FXVnDwDRpMJLzcX+3M1GDh95gwNGljve4P6DYiMjLRZJzLyBA3qWw/ladiwAadPn8ZozG/Y16xZg9NnznDy5EkAYmJi2LN3D00aN7LUqVmzBgcPHuLy5fxhZefOnePY8eM0vi6mxPtjNBF5OY7mVcOsyptXDeNQVEyR9ZbvPsblxFRGdmlm93s7SnrSZbLS4ylbpWAom0brREiFxsReKP6xf/n0DlLizhNcoeDzDApvSPSZHaTE51+YSbhygtio/ZSv1qbEeRoMBs6eOUW9+tb/X/UaNOJEpO1GyckTx6nXwDq+foPGnD190nLs3CgvNxeTyYi7h+1eJJPJxLYtG8nJyaFq9Zol3o/SplKrSuXxX3VHhzOpVI79YJOz8jApCr6uzlblfm56EjNzbNbxd3NhfNfGVA/yIc9k5vdjUYz4dROzH+5AwyLmUUzZcohAdxea3nDC81+m8fBEpdFgSrMeNmNKS0bjdeuxrBovH1xrNyRuxqdFxugjquBULpz476bcdr430vn4oNZqyUtItCo3JCTi5O9vs07Stu0ED+xH/PqNZByLxKNWDYIH9EXtpEPn401efAJeDeoRPLAfe3vb7s1wFJWLGyq1BiUz3apcyUxH5Wb7j4Ha2w9NaAUUo4Gs5d+icnHDpfMgVM6uZK/5+Y7meyPLd9fthu+uq/NNvrvOjO/aiOplrvvuLtjM7IfaW31303Pz6Dr9NwwmE2qVinGdG9LMgd/dlPRMTGYzvp4eVuW+Xh4kpKYXUcvaj39sJic3j85N6tl8/ejZC5y9HMv4pwbfbro2ZaXlN9xdPa3nebl6+pOedOWmdXOz05k9vg0mYx4qtZoOAycQVq2lVUzClZP8+vkQjMZcnPSuPPDUNPyCKxWxxZJJzsj65/O3Hnbi5+lGYmrGTet2HfsJyemZmExmRvTuQP82RZ9QT1myjkBvT5rWqGh3rmlpaZjNZny8va3KfXy8SUq2PeQwOTkZH58b4r29MZlMpKal4efrS7u2bUlNTeXVsa+hKAomk4lePXsweFDB786ggQPJzMzi6REjUKvVmM1mhj32GO3btbN7f5IzszGZFfxuaPz6ubuSkJ5ls86F+GS++v1v5j4/EK2m9K9TZqX/c+y7Wx/7Lh5+ZCTf+tif/15bzP8c+637TaDcdY2R+u2fJi8nnV8+7YFapcGsmGja7WUq1+9V4jzT01Ixm814e1sPj/T29iGlyGMnifo24k0mE2lpqfj6Fp7XOX/eLHz9/Klbr6FV+YWoc7zx6nPk5eXh7OLCG29Pplz58BLvh/hvKZWJ1bm5ueTmWl9pLskysDe2TRQFimquhPt5Eu5XcJJVN9Sfq2lZzN99wmYjYt6uSNZEXmT2Qx3Q3+Fu1vvSjf+lKpWNwsI8WnXCnJVB5v6dRce06UzepShyz5+6vRxv5sZjUqUq8jiNmjYLpwB/Gi76AVQqDAlJxCxdSdgzT6CYzGjcXKn+vw84+dYkDMkpdy5nKzfmX7io4DUVKApZq76HvPwT9exNy3Dt8yTZfy4Co+GOZmozpRueKxT+Pl9j87ubns383SetvrtuTjp+fbwL2XlGdl24ymebDlLW251G5W1fJLA79xsSVRSlWBdK1uzYz6yl6/jslSfx9fKwGbNiyy4qlg2iVsUwm6+XVOSelWxYMMHyvO+Imf/8y8aP5y046d0Y+vpy8nKzuHRqB1uXf4SXfznKVS6YdOkTGMHQ15eTk53GmYPrWPvj6wx88UeHNSSgiN/9W3z83732FFm5eRw5d4kpS9ZRLtCX7k0L9zzO+2Mba3YdZvbY4eh1DuipK/GxcmO8demhw4f5dcECnhs1impVq3Il5gozZs7Cx+cXHnn4IQC2bN3Kxk2beP21sYSVD+PsuXPMnDULPz9fOncqvMDFbexO/vfWRpzJbGbcT2t4tmszwgMKzxW6G07t/40tSwqO/Z5Pzsj/h62dKPLMIZ+T3o1BryzDkJvF5TM72P7bR3j6lSW0Yv6xf+bQak7t/41OD/8P3zKVSLhygr9XfoCrZyDVGvWzbwcKHTs3P84L/S798wdBZWPfli3+hb+2bOTdj76wTMi/JiS0HJ9P/ZbMzAx2/L2VKZ9/xHsff3nfNST+y3ePLg13tBGRnm77Kt2HH37IpEnWK+9MmDCB18rffHs+rk5oVKpCVy6TsnILXeG8mdohfqw+dqFQ+fxdJ5iz4zgzBrenSqB3sbf3X2BKT0MxmdB4Wf9h0Hh4Y0pNuWV9j9adydi+qchx9yonPe5N25C07CdHpFuIITkZs9GIU4B1r4POzxdDYqLNOubcXE6Mm8DJ8e/i5O9LblwCIUMexJiRgSE5GfdqVXApF0rtmQU9J9d+wNpG7mNX1z7kXHTMHAklOxPFbCrU66By9UDJSrNdJyMNc0aqpQEBYE6MRaVSo3b3xpxS9LAyRyv6u5tTqGfxZvK/u1FWZWqVivI++SfnVcv4cD4xje92RjqsEeHt4YZGrSYx1fpzTk7LwM/T9qTMa9btPMC73y7g4xeG0bRWFZsxObl5rNt5kJEPdnNIvpA/TyE4vOBk2WjMH5aXlZaAu1fB55KVnoirp+2euGtUajXeAfmNm8Cy1UmKPcue9bOsGhEarZMlJqh8bWIvHuHAlvl0GjL5tvfFx931n8/futchKT2zUO/EjUIDfAGoXDaIxLQMZq7cVKgRMX/tX8xZvYUZrz5BlXK314Pl6emJWq0m+YYrxykpqYV6J67x8fEpHJ+agkajwdMz//s+/4cf6dChA927dQUgIiKcnJwcpkz9moeGDEatVvPtnO8YNHAg7dq2tcTExcWxYOEiuxsRPm4uaNSqQr0OSRlZ+NlYdCQzN49jl65yIjqOj5blr6xmVhQUBRqM/YpvnulP08rl7MqluMJrtKdM+YLVjEzXjv30BNw8C4797IxEXDxuvgKjSq3Gyz//uPYPrU5y3DkObJxlaUTsWPUpDdo/TeV6PQHwC65KRvIVDmycVeJGhIenF2q1mpTkJKvy1NRkvLxtN8h8fHxJvjE+Jf/Y8fC0/luxfMkCFi/8iUnvf0Z4ROHeNp1OR/A/E6srVa7KmVMnWLViCc++8GqJ9kP8t9jViIiIiLjpVZVz587dtP64ceMYPXq0VZler8f00wc3rafTaKge5MPOqFg6VClYhm9nVCztKt96ublrTlxNxt/d+sTl+12RfLv9ONMGtaVmsG+xt/WfYTKSG3UGl5r1yNpfMK/ApWY9Mg/sumlV52q10QWFkDa16Anibk1agU6X39C4AxSDkYxjkfi2bEbC+o2Wct+WzUj4c/PN6xqN5MbGAVCmZzcSN23Nv8J/9jy7ezxoFRvxynNo3dw4/d4nNld8spvZhCn2EtqwahhPF0xo1IZVxXDmiM0qxuhz6KrWB50TGPL/kKp9A1HMZswZKY7LrRiK/u5epV2lkn53bz5mXQHyTKabxpSETqulWnhZdh09RftGBScnu46eom2DoscMr9mxn3dn/8r7ox6lVb0aRcat33UQg9FI9xYNi4wpKSdnd6tVZxRFwdUzgAsn/yawXH4uJmMe0Wf30Kr3mBJtW0GxnJjdLOrWMcWj02qpHhbCzuNn6NCg4HPcefwM7epVv0nNGzJSIO+GceLfr9nGt79vZtrLj1MzvPjHYZG56nRUrlSJAwcO0LJFC0v5gQMHaNbM9vyA6tWrsWuX9QTx/fsPULlyZbT/zL3Kzc1BfcPfXLVajaIolp7U3Nxc1GobMWaz/fuj1VC9bCA7T12kY+2CXqWdpy7Srmbh5Y3d9XoWjxlqVbZw+2F2n77E/4b1JNTXy+5cisvmse8RwOVT2wkILTj2r5zbQ7MeJTxBVqyPa6MhG1TWV75VajWKUvLPXKfTUbFSFQ4d2EuzFq0t5YcO7KNJs5Y261StVoM9u63n+R08sJeKlatajh2AZUt+ZfGvP/LOu59QqXLVYuWjoGAw3P3e6tul1vx35yeUBrsaES+//LLVc4PBwIEDB1izZg1jx469ZX29Xm9z+JLtEZbWhjauxturdlIjyJc6IX4sPXSW2LQsBtTL/4GbsuUQcenZvNcr/wf7pz0nCfFyo4K/F0aTmd+PR7Hh1GX+17fgSzlvVyTTtx3hgweaE+LlRkJGNgCuTlpcne7+JNRrNG6uuFUq6J5xjSiLZ91q5CWlknOp6Eltd0rq2uUEPjOavKgz5JyJxLNdN7R+AaRvyl91xGfAMLQ+fsTP/tyqnkebzuScPYEhunDvjyWmdRey9u/EnFm8Meb2uPTdD1T/9H3Sjh4n7cAhQgY/iD44mOhfFgFQ4dUX0ZcJJPK1twFwCQ/Ds04t0g4dQevlSbknHsWtciUiXxsPgDkvj8zT1quCGf/pfbux3BHy9m7CpeejmGIvYrpyHqe6LVF7+pJ3KH8lK33rB1B7eJO9+gcADJF7cW7eDdfuQ8n5ezUqFzec2/bFcGRnwVAmtQa1/z9XXzVa1O5eqANDIS8Xc4pjVzga2qgqb/++K/+7G+rP0oPXvrv5V8WmbDlMXEYW7/X857u795/vrp8XRrOZ349dKPTdnbPzODWDfCnr7Y7BZOavczH8fiyKcZ0dd0IO8Ej3trwz42eqR5SjTqVwlm7aQWxiMg92zD9R/HrBKuKS05g88mEgvwExYebPjBnaj1qVwkhIye/FcHbS4e5q3QhasWUXbRvUwtuj6GVub5dKpaJB28fYs34mPgHheAeEsXv9TLQ6Z6o1LBi/veaH13D3KkOr3vknV7vXzaRM+Vp4+ZfHbMrj/LGtRO5eQYdBEy11/vrtc8JrtMHDOwhDbiYn96/m8und9Hv2W4flP7RzS96es5ga4aHUqVCOpVv3EpuUyoB2+RO8pyxZR1xKGu8NHwDAgo07CfL1Jjw4v5fl4OkL/LDuL4Z0KDiRn/fHNqav+JMPnh5EiL+3ZX6Lq94J19uY3N6/Xz8+/ewzKleuTPVq1fhjzRri4uPp2SN/Fbvv5s4jMTGRsWPyP+OePXqw8rdVzJw1m+7duhJ54gRr163jjddes2yzaZOmLFu2jIoVK+YPZ7oSw/wffqRZ06ZoNPnDbps2bcKvvy4gICCAsLAwzp49y7Jly+jSpbPd+wLwaJsGvPXLWmqULUPd8GCW7DxCTHI6A5vnN6i/+v0v4lIzef/hrqjVKioHW/ds+bq7oNdprMoNRhNnr+b3ABtMZuJSMzkRHYer3ony/t63le+NVCoVdVo/xv6NM/HyD8MrIIz9G2aidXK2mruw4ZfXcfMKtDQs9m+cSUDZWnj5lcdkMnAxcgun9q2gdf+CoVLh1duzf+MMPHyC8SlTiYToSA5tnUe1xg8WyqM4evcbyFeffUjFylWpWq0m69esIiH+Kl17PADAD/Nmk5QYz0uvvglA1x69Wb1qOd/Nnkbnrr04eeIYG9atZvQ/f8MgfwjTzz/MZfRrbxEYGERyUn7PhbOLCy4u+b9FP34/mwYNm+IfEEh2dhbbtmzk2JFDjJ/8sV37If477GpEvPTSSzbLp02bxt69e28roVvpWr08qdm5zPr7KAmZOVTy92LqwDaEeOX/AU7IyCY2LdMSbzCb+WLTQeIystFrNVT092TKgDa0rhhiiVm4/zQGk5mxy62XaxzRsiYjWxW+Edrd4tWwFs03/GB5XuN/+T8cl+Yv5fDwcXc9n8zd20h098C7zxC0Xr7kRV8g9vOJltWWtN4+aP2sV8VRubji1rAFiT/PLnK7ujIhuFStScynbxcZ4whxq9ei9fYi/Lln0AcGkHnqDIeffo7cK/kNMqdAf/QhBcMZVBo15YY/hmtEGIrRSPLOPewb/Bg50TefjHenGE7uz28ItOiGyi3/ZnOZS75B+Weyu9rdC7XHdd3ehjwyF03DueMA3B8di5KdieHkAXL+KljWUOXuhcewNyzP9U06oW/SCePF02QucOwE967Vy5Oak8us7ccKvrsDWhd8dzOziU0ruJRgMJn5YtMh6+/ug62tvrs5BhMfrNtniQn39eC9ns3oWv0WYyNLqEuz+qRmZPHt8nUkpKRRsWwwX415mmD//F7LhJR0YhMLhqQs3bgDk8nMx98v4ePvl1jKe7VqzMQRD1meX4iJ4+Cp83z92giH5mtLo05PYzTksmHRJHKzUgkKq0v/Ud9ZXbVNT45Bdd2VVUNeFhsXTSI9JRatzhnfwAp0e+xTqjYoWNY5Kz2BtT+8RmZqHE4uHviHVKXfs98Wmnx9O7o2qU1qZhazfttEQmo6lULKMPWlRwnxyz/eE1LTiU1MscSbFYWpS9cRnZCMVqOmbIAvL/TvwoC2BatKLdy8C4PRxNhvfrF6rxEPtGdkn45259q2bRvS0tP46edfSE5KIiw8jHcnTaJMmfyhNEnJScTFFwwlDAoK4t3Jk5g5azarVq3C18+PZ0eMsNwjAuDhh4agUqn4fv4PJCYm4uXlRdMmTXh8WMG9CEaNHMn8H35k2rTppKSm4ufrS/fu3S1zJuzVrX5VUrNymLV+J/FpWVQK9mPaU30I8c0fLpOQlklsiu0hlUWJS8tg8OcFizt8v3kf32/eR6OKocwZNfC28rWlXrunMBpy2LZsMrnZqQSWr0Ovp+dYHfsZKVesRlgY8rLZtmwyGf8c+96BEXR86BMq1Ss49lv1fZvda6ewdelksjMScfMMpEazwTTqNMquPFu16UB6WhoLf5lPclIS5cPCeXvSRwQG5v9dSk5KJD4+zhJfJiiYtyd9yNzZ0/lj1Qp8/fwYPuIFyz0iAP74fQVGo4FPPpho9V6DHx7GkEceByAlOZkvP/uA5KQkXN3cCA+vwPjJHxdaKUqIG6kUB95W+ty5c9SrV4+0tJL9oFyT9d2EWwfdo1yfnMTvuuJ1E95rehpOcu7xkq8mca+oMG8VmyrfmRt03Q3tTx8i9dMXSjsNu3iNnUrWnHdKOw27uQ6fTPru30s7Dbt5NOnJjLWlnYV9RnaFrG2LSjsNu7m2Hsj5s47vcbxbIipWImfVN6Wdhl2cez3Llysddupy173cW8XxM6VzMcoRalQKuXVQKSmtc5kK84q+58i/mUOnsS9evBhfX5lPIIQQQgghxL+ZXcOZ6tevb9XtpygKsbGxxMfHM336dIclJ4QQQgghRHHIEq93l12NiL59+1o9V6vVBAQE0K5dO6pVK97di4UQQgghhBD3J7saERMm3L9zF4QQQgghhChN06dP59NPPyUmJoaaNWvy5Zdf0rp1a5uxjz/+ON9//32h8ho1anDs2DEA5s2bxxNPPFEoJjs7G2fn4t+PqSSK3YgoyWRpzxtuciKEEEIIIcSdpFLfH/eJWLBgAS+//DLTp0+nZcuWzJw5k+7du3P8+HHKly+8uuBXX33FRx99ZHluNBqpW7cuAwdar2bm6enJyZMnrcruVAMCStCI8Pb2vukN5q5ncuCNnoQQQgghhPi3+Pzzzxk+fDhPPfUUAF9++SVr167lm2++4cMPPywU7+XlhZdXwc0aly9fTnJycqGeB5VKRVBQ0I3V75hiNyI2bSq4k3BUVBRvvPEGjz/+OM2bNwdgx44dfP/99zZ3XgghhBBCiDuptHoicnNzyc3NtSor6sbKeXl57Nu3jzfeeMOqvEuXLmzfvr1Y7zdnzhw6depEWFiYVXlGRgZhYWGYTCbq1avHu+++S/369Uu4N8VX7EZE27YFNy+ZPHkyn3/+OQ89VHATm969e1O7dm1mzZrFsGHDHJulEEIIIYQQ96APP/yQSZMmWZVNmDCBiRMnFopNSEjAZDJRpkwZq/IyZcoQGxt7y/eKiYnhjz/+4Oeff7Yqr1atGvPmzaN27dqkpaXx1Vdf0bJlSw4dOkTlypVLvlPFYNfE6h07djBjxoxC5Y0aNbJ0zQghhBBCCHG3lNYSr+PGjWP06NFWZbZ6Ia534xQBRVGKNW1g3rx5eHt7F1optVmzZjRr1szyvGXLljRo0ICpU6cyZcqUW27XHnZ92uXKlbPZiJg5cyblypW77aSEEEIIIYS4H+j1ejw9Pa0eRTUi/P390Wg0hXod4uLiCvVO3EhRFL777jseffRRnJycbhqrVqtp3Lgxp0+fLtnOlIBdPRFffPEFDz74IGvXrrW0enbu3MmZM2dYunSpQxMUQgghhBDi38DJyYmGDRuyfv16+vXrZylfv349ffr0uWndLVu2cObMGYYPH37L91EUhYMHD1K7du3bzrkodjUievTowenTp/nmm2+IjIxEURT69OnDyJEjpSdCCCGEEELcdffLEq+jR4/m0UcfpVGjRjRv3pxZs2Zx8eJFRo4cCeQPj4qOjmb+/PlW9ebMmUPTpk2pVatWoW1OmjSJZs2aUblyZdLS0pgyZQoHDx5k2rRpd2w/7GpEAJw/f56oqChiYmJYvHgxoaGh/PDDD0RERNCqVStH5iiEEEIIIcS/wuDBg0lMTGTy5MnExMRQq1YtVq9ebVltKSYmhosXL1rVSU1NZcmSJXz11Vc2t5mSksIzzzxDbGwsXl5e1K9fn61bt9KkSZM7th92NSKWLFnCo48+yiOPPMKBAwcsy1qlp6fzwQcfsHr1aocmKYQQQgghxM2U1sRqe4waNYpRo0bZfG3evHmFyry8vMjKyipye1988QVffPGFo9IrFrs+7ffee48ZM2Ywe/ZsdDqdpbxFixbs37/fYckJIYQQQggh7j12NSJOnjxJmzZtCpV7enqSkpJyuzkJIYQQQggh7mF2NSKCg4M5c+ZMofK//vqLChUq3HZSQgghhBBClIhKVTqP/yi7GhEjRozgpZdeYteuXahUKq5cucJPP/3EmDFjihzfJYQQQgghhPh3sGti9WuvvUZqairt27cnJyeHNm3aoNfrGTNmDM8//7yjcxRCCCGEEOKm7pclXv8tVIqiKPZWzsrK4vjx45jNZmrUqIG7u7sjcxNCCCGEEKJYol8aXCrvG/rVglJ539Jm930iAFxdXWnUqJGjcuFgl9YO29bdVm/dNs493qu007BLhXmr+F1XtbTTsFtPw0m2VK9X2mnYrW3kQeLffqK007BLwHtzOd6vY2mnYbcayzaQ/OH9OwTTZ9x0+o46Vdpp2GX59Cpk/b2ktNOwm2vLB9l0JLu007Bb+9ouJL03orTTsIvv2zOZMN9Q2mnYbdJjOlL/91Jpp2E3rzG271NwL7iflnj9N5BPWwghhBBCCFEi0ogQQgghhBBClMhtDWcSQgghhBDiXiATq+8u6YkQQgghhBBClEixGxErV67EYLh/JzIJIYQQQoh/L5VaXSqP/6pi73m/fv1ISUkBQKPREBcXd6dyEkIIIYQQQtzDit2ICAgIYOfOnQAoioLqP3ybbyGEEEIIIf7Lij2xeuTIkfTp0weVSoVKpSIoKKjIWJPJ5JDkhBBCCCGEKA6ZWH13FbsRMXHiRIYMGcKZM2fo3bs3c+fOxdvb+w6mJoQQQgghhLgXlWiJ12rVqlGtWjUmTJjAwIEDcXV1vWn833//TaNGjdDr9beVpBBCCCGEEDcjPRF3l11TyidMmHDLBgRA9+7diY6OtucthBBCCCGEEPeoO3qzOUVR7uTmhRBCCCGEyPcfXm61NMinLYQQQgghhCgRaUQIIYQQQgghSuSODmcSQgghhBDibpB7mN1dd7QnQv4zhRBCCCGE+Pe5LydW+z3Ql8CBD6Hz9SPnQhTR30wh8+jhIuN9OnQmcODD6EPLYsrMJG3vLq7MmoYpPc0So3FzJ+iJp/Fu2RaNhzt5sTFEz5xG+p6dDs/fs0MPvLr3R+PtiyH6Iok/zybn1DGbsQFPvYxHq06FyvOiL3D5recACH7jQ1yq1S4Uk3VoD7FfTHJs8sXk26oRFV4djleDWjiHBLL3wVFcXbmhVHK5XshDgyj75DD0Af5knjnL2Q8/JXXfgaLjHx5MyMODcQ4NITcmloszv+XqilU2YwN6dKXGZx+T8Ocmjr3wyh3J37lJe1xbd0ft7o0xLprM1T9juHC66AoaLa7te+Ncrzlqdy/MaclkbV5Fzv5tAHgNfx2niGqFquWePETaD186PH+fbr3x6zsIrY8fuZeiuDpnOlmRR4qM92zTEf++g3EKCcWUmUnmgT1c/X6m5bvr3bkH3u26oC8fDkD22VPE/TSHnNMnHZ67vkEb9E07oXb3whQfQ/afizBePlt0BY0W55Y9cKrVGLWbJ+b0FHK2ryHv8A4AnOq2xKl2UzT+IQCYYi+SvWUFppgLDs/9miE9/ejS0gs3VzWno3KYuSCOSzF5Rca/93JZalUpvBLf3qMZvDf9CgDdWnvRrY03gb75f04uxuSxcHUi+49nOTT3hRt38v2abSSkpFMxNJAxD/WkQZUIm7EHTkXx1eI1RMXEk5NnINjPmwfbNWFol1aWmLPRV5m+/E8io6KJSUxhzJCePNKlpUNzvp6iKKxaOIO//lxKVmYa4ZVq8dDT4wgpV6nIOlcuneG3X7/hwrnjJMXHMPDxMXTsNdQqZs3SORzYtYHY6CicnPRUqFqXfkNfJig03GG56xu2xbl5l3+O/StkrVuI8dKZoitotLi07olT7aaWYz/7r9XkHdpeKNSpRiPc+z9N3smDZCz6xmE536hdXTUNK6txcYLLCQq/7zIRn1q8urXCVQxsoyXyoplfN1vfTNfDBTo31FA5VIVWA4lpsGK7kZikO7AT/3Cq1wp94w6o3DwxJ8SSvWkppuhzRVfQaNA374ZTjUaoXD0xZ6SQu3MdhqO77lySpUQlE6vvqjvaiEhPT3f4Nr3bdiB05Itcnvo5mceO4N+zNxXe/5QTTz2KIT6uULxbzdqUH/sW0TOnkrZzOzo/f8q+NIZyo18natJbAKi0Wip+9DmGlBSi3h1PXkIcTgGBmLId+0cQwK1Ja/wefpqE+d+Qc/o4nu27EzR6IpfeHIUpKb5QfMJPs0haNK+gQK2h7LtTydzzt6Xo6tT3UWkL/ivVbp6UfXcqGXv+cnj+xaVxcyXt8Ekuf7+Uhou+LrU8rhfQvQsV3xjL6Xc/IG3/QYIHD6D2zGnseaA/uTGxheKDhwwk4pUXOPXOZNKPHMOjTi2qTH4HY2oaiZu3WsXqQ4KpOHY0KXv33bH89bWa4N7jYTJ++wHDxdM4N26H12OjSZryFuZU23+xPIeMQu3uSfqyuZgSr6J28wS1xvJ62s9fg6bgudrVHZ/nJpN7dI/D8/ds2Y6gJ0cRM2sKWSeO4tOlF+XHf8iZF5/EmFD4u+tSvRahL77O1bnfkL5nB1o/f4JHvkzwqFe5/PEEANxq1iV120ayThxDMeTh128wYRM+4eyLwzEmJTgsd131hrh0GkDW2l8xXj6Hvn4r3Ac/R+rsd1HSkm3Wces7HLWbJ1mrf8ScHI/K1cPqs9eGVcZwfC/Zl8+hGA04N+uM+5AXSJv9LkpGMc9uSqBfZx96d/Bmyg9XuXI1j4HdfZn0QllGTTpPTq7tCz4fzbqCVlvQo+zhpuHLN8PYvj/DUpaYYuSH5QnExOc3Rto382TcyFBGf3jhpg2Ukli7+zCf/vI74x7tTb1KYSzZvJvnv/ieJe+9TLCfd6F4F70Tgzs0p0q5IFz0Thw4HcV73y/HxcmJB9s1ASAnz0DZAF86N6rFZ7+udkieN7Nu+Tw2rPqRYc9NJjAkjD8Wz+aryc8yacpynF3cbNbJy83Bv0woDZp3ZtG8/9mMOXV8H227DSa8Uk3MJhMrfv6aKe8+y4Qvl6J3drntvJ1qNMK1yyCy/vgZw6WzODdog8dDL5A6YyLmIo599/5Po3bzJHPVfMxJ8ajcPGye3Km9fHHtNADDxZtcCHGAVjXVNK+uZvl2E4lpCm1qa3iss5apy43kGW9e18sNujTUEHXVXOg1ZycY3l1LVKzCj3+ayMxR8PFQkeOYw94mXdX6OLfvR/afizBFn8epbgvcHhxJ+twPUdJt/3+4PvAEKlcPstb8gjklAbWru6xiJBzCrqMoIiKCChUqFPm4kwIeHEzSmt9JWrOK3EsXiJ4xFUN8HP4P9LMZ71q9JnlXY0lYvoS82Bgyjx0h8feVuFapaonx7doTjYcn5yeOI/P4EQxxV8k8doSccze5ymgnr659Sd+6nvSt6zDEXCbx59kYkxLw7NDDZrySnYUpNcXy0EdURu3qTvq29ZYYc2aGVYxLrXooeblk7i69RkT82q2cmvAlscvX3zr4Lik77FFily4jdvEyss6d5+yHn5ITG0vIkIE248v07kXMgiXE/7GOnMvRxK9eS+yS5ZR76gnrQLWa6p98QNTX35Bz6c7dF8WlZRdy9m0lZ99WTPExZK7+BVNqEi5NOtiM11WuhS68Kqnzv8Bw9jjmlESM0eetriAq2ZkoGWmWh1PFmiiGvDvSiPDrPYDkDX+Q8udq8i5f5Op30zEkxuHb7QGb8a5VqmOIv0rS78swxMWSHXmU5LWrcKlUxRIT/eWHJK9ZSW7UWfKiLxEz/XNQqXCrU9+huTs36UDeoe3kHdqOOTGW7D8XY05LQV+/jc14bYUaaMtXJmPhNIxRJzGnJmGKuWB1tTBr5Txy92/FFHcZc9JVsv74CZVKhS68cM+QIzzQwYdFa5LYeTCDizF5fDX/KnonFW0aexZZJyPLTEqayfKoV82V3Dwzf+8vuEC050gm+45lciXOwJU4Az+tTCQn10zVCGeH5f7j2r/o27oh/ds0pkJIIGMf7kWQrxeLNtm+klotLITuzepSMbQMIf4+9Gxenxa1KnPgdJQlpmZEWV4Z1J1uTeui02psbsdRFEVhw+8/0b3/U9Rv1pHQ8pUY9sK75OVms3vbH0XWC69UiwcfG03jVt3Q6nQ2Y158ezot2vchpFwlyoZX5bHnJpGUEMPFc8cdkrtz007kHvyb3IN/Y06MJWv9QsxpyegbtrUZr6tQE21YFdJ/nYrx/AnMqYmYrkRhvHzDlXKVCre+w8na+hvm5MIX0BypWXU1246YibyoEJcCy/42odNCnYibnwKpVPBgaw2bD5lItnFNtFUtNWmZCsu3m4hOVEjJhPOxCskZhWMdxalRO/KO7MRwZCfmpKvkbFqGOT0Zp3q2e9G04dXQlq1I1tKZmC6eQklLwhR7EdOVqDuXpPjPsKsn4uWXX7Z6bjAYOHDgAGvWrGHs2LGOyMsmlVaLa+UqxC340ao8fd8e3GrUslkn8/hRgh9/Go/GzUjfsxOttw/erduRtmuHJcareUsyI49R9oXReDVvhTE1heSNfxK38CcwF776YDeNFn14JVJ+X2xVnH30AM6Vinfi4NGmC9nHD2JMLPpH17N1FzJ2bUXJy72tdP9NVDotHjWrc/Hb76zKk//eiWf9ujbrqJ10mG/4DM05OXjUroVKq0Ux5l/CChs1AkNyMrFLluPVsMGd2QGNBm1IOFlbra+Y5p05hrZ8RZtV9NXqY7xyHtfW3dHXa4GSl0veiYNk/rkUjAabdZwbtiH3yC4wOPhSmlaLc8UqJCz9xao44+A+XKrVtFkl68QxAh55EvcGTcjYvxuNlw+eLdqQvq/oLni1kx6VRospw4G9oGoNmqDy5OxYZ1VsOB+Jtqztiya6ynUwxVzEuVlnnGo1RTHkYjh9hOytvxX52aNzArUGJSfTcbn/o4yfDl8vLQcjC3pXjUaFo6ezqVbBmXV/Fa/no1MLL/7al05unu2eC7UKWjTwwNlJxYlzOQ7J3WA0EnnhCk/0sD5pbVazEofOFG/o14kLVzh05iKj+nd2SE4llRAXTVpKAtXrNreU6XROVK7RiHMnD9KmywCHvVd2Vv4ZrKu71+1vTK1BE1ye7O1rrIoN546jLWv7d0dXpQ6mmAs4N++KvnZTFEMeeacOkb1lpdWx79K6F0pmOnkH/0Z3kyFdt8vHHTxcVZyJKfhbbjLDhasK5QJV7L1JJ0i7OmqycmD/GYXygYVfr1pWzdkrZga10RBWRkV6Nuw5aWLf6Tt0jyy1Bk2ZcuTush4abIw6iTYkAlt/8bWVamO6egmnxh1xqtEIxZCH8exRcv5eXfRv0X1M7lh9d9nViHjppZdslk+bNo29e/feVkI3o/H0QqXRYki27rIzJCfj4eNrs07W8aNc+Phdwt+ahNrJCZVWS+r2bVye9qUlxik4BPd6QSRvXM+5t8eiDy1H2edfQaXRcPWneY7L38MTlUaD6YYuYFNaMhqvW598arx8cK3dkLgZnxYZo4+oglO5cOK/m3Lb+f6b6Lx9UGm1GBKsh/0YEhNx8ve3WSf5rx0EDehHwp+byDgeiXvNGgT174vaSYfOx5u8+AQ869cj+MG+7O03+I7mr3b1QKXRYM5IsypXMlNRu9tuQGt8AtCVr4JiMJD201TUbh64P/AoKhc3MpZ9VyheGxqBNqgs6TZeu11aDy9UGg3GlBuO/ZRktN62v7vZJ48T/cWHhI4Zj1qX/91N3/03sbOnFvk+gY89jTEpgcxDjhtWpnJ1R6XWYM60bpgomWn5w8Ns0Hj7oS1XEcVkIGPJTFSu7rh2GYLK2ZWs1T/arOPSri/mjBQM5084LPdrvL3yr7SnpFuP3UhNNxLga/sK940qhzkTFqrn6x+vFnotLMSJj8aUx0mnIifXzEezYrgc65iGaHJ6FiazGV8vd6tyP08PElNvPgym66sfkZyeiclkZkSfjvRv09ghOZVUWnL+0DrPG451T29fkuJjHPY+iqKw+PvPqFStPqHlb//E/Nqxr2Ra/+6YM9PRuds+9tU+AWjLVUIxGkhfPAO1izuu3R9C7eJG5qr5AGjLVkRfryWps9+97Rxvxd0l/6QyM9u6PCMbvN1tVPhHuQAV9SupmbGq6PFOPh7QqKqaHcfNbD1qoqyfmu6NNRhNJg6dc3xDQuXilv//kXXD34GsdFRuHjbrqL380IRWQDEayVoxB5WLOy6dBqBydiV77S826whRXA6dE9G9e3fGjRvH3LlzbxqXm5tLbq51m1mv1xf/jW6YsJ2/CJTtL6y+fDhlR71E7E/zSN+7G52vHyFPj6LcS2O49PnH/2xAjTElhUtffgpmM9mnT6Hz8ydwwEMObUQU5H/Dc5WqyPyv59GqE+asDDL3Fz3Z26NNZ/IuRZF7/tTt5fivdcPnrFIVOp6uufDNLHT+ftT/dT4qlYq8xCRil6+k/FNPoJhMaFxdqfbJ+5x6ZzLGlJQ7nzpg4+ApOlSdf1ylL5qFkpv/FzTjj1/xHDKKjN9+KHQVyrlRG4yxlzFGn3dsyjejosjP36lsGEFPPUfCwh/IOLAXrY8vZYaNIHjkK8RMKzw+3K/vYLxatSdq/Ksohjtxhc3GsVPU91alBkUhc+VcyM2/Ip+9YQlu/Z8ia92CQp+9vmlnnGo0IuOnL8F0i0HaxdCmsQfPPlTG8vy9b6Jt7gKoivr4C+nUwpML0bmcvlC4hyH6ah6vfHgBNxc1zet78OJjZXjri8sOa0jkZ2p9rCuKwq0WAPzujWfIys3jyNlLTFm8hnKBfnRvZrvn0ZF2bf2dn2e9Z3n+3Lj8hu+NKxYqisItd6IEfv32Qy5fOMXY9+Y5bJuA7cO8qEP/n9/UzOVzUHJzMAFZ6xfjPuAZMtf8Amo1bn2fJPP3H1CyHd/rVjtCxQPNCoan/bTRZDPdm/z046SFB1tpWLnDRNZNOvRVwJVEhQ0H8ns5YpPMBHhD46pqDp0zFV3xdpXg/+Pajmb9Ph/y/vkt2rwc195PkL1h8b+vN0LmetxVDm1ELF68GF9f21cVr/fhhx8yaZL1qkETJkyg7y3qmdJSUUxGdDe8h9bbB+MNvRPXlBkylMxjR4hflN/izjl/lss52VT+Yjox877FmJSIMSkxf2jKdUOXci5GofPzsxq2crtM6Wn5J59ePlblGg9vTKkpt6zv0bozGds3FXmSoXLS4960DUnLfnJEuv8qhpRkFKMRnb+fVbnO15e8xESbdcy5uZx6eyKnJ76Hzs+XvPgEggc9iDEjA0NyCm5Vq+BSNpRa078qqPTPD1ibI3vZ3aMvOZcuOyR/c1Y6ismE+oYhCio3T8xFTMI1p6diTku2NCAATPFXUKnVaLx8MSVed0VZ54S+dhOyNix3SL43MqanophMaL1vOPa9fDCm2v7u+j/4ENknjpG4fCEAuRfOEZObQ8QHXxH/83cYkwt6lfz6DMR/wMNcmDCW3As3WaXEDkpWBorZhNrNk+tPC1SuHoV6J64xZ6RizkixNCAATImxqFRq1B7eVmPA9U064dyiKxm/TMEU75g5NbsPZ3AqquC9df9Mjvb21JKcVrAXXh6aQr0TtjjpVLRq5MEvq2x/V4wmiI3PPxk5ezGXymF6HmjvzTe/FJ4wX1I+Hq5o1GoSU60/66T0DHw9b3IpGQgNyP9bUblsEIlp6cxcseGuNCLqNm5HROWCFfOMxvzGVGpyIl4+AZby9NRkPL1u/TezOH6d8xGH927h1cnf4eNX5tYViuHasa+6oddB7eaB+YbeiWvMGamY01NQrj/2E2L+OfZ9UDk5ofH2x33wcwWV/mlI+bw5ndRv3sGcbP+iCCcvKUQnFBzTmn/OKd1d8nsfrnFzhswiRtz5eoCPh4qHOxQ0Rq619d4Zmj8hOzkjf3s3rvCUkAo1wu7MkBolOzP//+OGHlCVqwdKlu3fIiUzLf9vRF7BzpoTr+b/f7h7Y065s/NRxL+bXY2I+vXrW11RURSF2NhY4uPjmT59+i3rjxs3jtGjR1uV6fV6Ih+4+RKgitFI1ulTeDRoTOrf2yzlHg0ak7rD9iRitV6PYrK+IqDcMM8h89gRfNp3sro0oQ8thyExwWENCABMRnKjzuBSsx5Z+wvmZLjUrEfmgZsvteZcrTa6oBDSphY9UdmtSSvQ6fIbGsKKYjCSfiwSnxbNSfyz4PPxadGUxI2bb17XaCTvav7JUGCPriRu3pZ/Zefcefb0ftAqNuLF59G4uXLmw0/IjS284pPdTCaMV6JwqlSTvMj9lmKnSjXIizxos4rhwmn0NRuBkx7+mduh8QtCMZsx3bCak75WY1QaHTkHCy/B6BBGIzlnT+FWtyHpuwpWFnOv25D03X/brGLru1vQ0C/4/fHrOwj/AY9wcfIb5Jy9Az1wZhOm2ItoI6pjOHXIUqyLqEbeKdtLSxsvn8WpWgPQ6cHwz2fvG4hiNmNOT7HE6Zt2wqVFd9IXTMUUe9FhKefkKpaT+muSUo3Uq+7K+cv5+Wg1UKuyC98vv/UJW6uGHui0Krbstn3ieCMVKkvD5XbptFqqh4Ww8/gZOjQsmD+z89gZ2tWvUeztKArkOfL3/CacXdysVlxSFAVPb38iD++gfIX8+W9Gg4HTx/fSb+jLt/VeiqLw65yPOLh7I6MnfYt/mdDb2p4VswlTzEV0EdUxnDxoKdZFVCfvuu/C9YyXzuJUvaH1se9X5p9jPxkUhdSZ1hcRXdr1QeXkTNa6BZiLuKhQXHlGSLrhfDo9S6FisJrYpPzfD40awsqo+HOf7TmPCakwbaX196dDPQ16Hfyxx0TaP1OLLsYr+N8wqsvPE1Iy7tCcCLMJ09VLaMOrYjxT8NujDa+K4YztpbKN0efQVamXP+fqn7luap+A/P+PjJQ7k2cpkjkRd5ddjYi+fftaPVer1QQEBNCuXTuqVbv1BGG9Xl+y4UvXiV+ygPKvvU3WqRNkHj+GX8/e6AIDSVi1HIDgJ0eg8/Pn4qfvA5C2czvlXnmNjF59Sd+7C52vP6HPvkDmieMYk/KvqiWsWo5/nwcJffYlElYsQR9aljIPPUrC8sVFpWG31LXLCXxmNHlRZ8g5E4lnu25o/QJI35Q/YdZnwDC0Pn7Ez/7cqp5Hm87knD2BIbroiYQerbuQtX9nkVdH7yaNmytulcpbnrtGlMWzbjXyklLJueS4McAlcfn7H6j20ftkHD1G2sHDBA96EOfgYK4syP9/jnjlBZzKBHLyjfEAuISXx6N2LdIPH0Xr6UnZx4fiVrkSJ954BwAlL4+s09YreBn/Wdb4xnJHyP57HR4DnsYYHYXh0hmcG7VF4+VH9p78RpFb5wGoPb1JX/ItADmHd+Lavjee/YeTuWE5ajd33LoNyr9HxA1d2C4N25Abuf+ODC+4JnHlYkJfyj/Rzzp5HJ/OPdH5B5K89jcAAocOR+vrz5Up+cMM0/fsJGTUaLK6PkDGwfzhTEFPjiL7VCTG5Pzvrl/fwQQ8/DjRn39AXlwsmn96Osw52Sg5jpnYC5CzeyNuDwzDFHMBY/R59PVaovb0Ie9A/sUM57Z9UHt4k7XqewDyju3FpWUP3Ho+Sva2Vahd3XHp0I+8w9stn72+aWdc2vQic+VczKlJlquLSl6u5eTLkX7bmMyArr5ciTMQE5fHgG6+5OYpbN1T0DB4aVgQiSlGflxh3bDo1MKTXYcySM8sfNI1tLcf+49lkZBswMVZTatGHtSs4sLkrx23UP7Qrq14e/YiaoSHUqdieZZu2UNsUioD/lmudcritcQlp/He0/krrS3YsIMgP2/Cg/Ov+h88dYEf1m5jSMeCic0Go5FzV+L++beJuJQ0Tl68goteT/kyfjiSSqWiY89HWLN0DoHBYQQGl2fN0m9x0rvQpHV3S9zcKW/j7RdIv0deBPIbGjH/3IvEZDSSkhTHpfMn0Du7Ehic//v6y7cfsGfbHzz7+pc4O7uR+s9VfBdXd5z0t79CVs6uP3Hr8wTGmAsYL5/DuUFr1F6+5O7PX+bapX1f1B7eZK6cB0Du0d04t+6B+wPDyNr6G2pXd1w7Pkjuob8tx74p/orVeyg5WTbLHWVnpJnWtdUkpikkpSu0rq3BYITD5wuO534tNaRnKfx5wIzRDHEpN3wO/4zMu758x3EzT3XX0LqWmmMXzIT6q2hYWc3KnXduKFPe3s249BhqWWHJqU4L1B4+5B3Kvxijb90LtbsX2X/kj0gwRO7DuVlXXLs9TM72P1C5uOPctg+Gozv/fUOZxF1nVyNiwoQJjs6j2FK2bETj6UnQI4+j9fUj58J5zr39Goa4/KEZOl8/nAILunKT1v+B2tUV/979CX3mOUyZGaQf3E/MtwU3tTHEx3F23GhCR75A1ZlzMSQkEL9scf7qTA6WuXsbie4eePcZgtbLl7zoC8R+PtGy2pLW2wetX4BVHZWLK24NW5D48+wit6srE4JL1ZrEfPq2w3O2h1fDWjTf8IPleY3/vQnApflLOTx8XKnkFP/HOnTe3oSNGoFTgD+Zp89wZOTz5F7Jb9Q4BQTgHBxsiVepNZR9/DFcI8JQjEZSdu3lwEPDyL1yZ/7Q3Uru0d2oXN1wbd8btYcXxqvRpP7wBeaU/BNqtYcXau/rTn7yckmd+ynuvYbi8+w7mLMzyT2yO391puto/MqgC69C5tyiJ+w7Qtrfm9F4eOI/6FG0Pr7kXozi4nvjLPd30fr4oQsoWAIlddNa1C4u+PToS5knRmLKzCDzyEHi5hd8D3y690atc6Lc6xOt3iv+1++JXzDfYbkbIveR7eKGc8seqN09McXHkLFwOua0/BNltbsnas/rhmoZckn/ZQquXQbh+cQbKNmZ5EXuy1+d6R/6Bm1QaXW493/G6r2yt/1Ozl+/Oyz3a5atT0bvpGbEkEDcXdWcisph4tTLVveICPDRopitr6KGBOqoUcmVCVNsD83z9tTy8uNB+HhqyMwxcyE6l8lfR3PohOPus9O1SR1SM7KYtXIjCanpVAotw9SXhxHin/+ZJ6SmE5uUYok3KwpTl6wlOj4ZrUZN2QA/XhjQlQFtm1hi4lPSGTKx4B4289dsY/6abTSsGsG3rz/tsNyv6dL3cfLycvhl9gdkZaYRUbk2L47/xqrHIikhxupKakpyHO+PHWJ5vn7lfNavnE/lGg15dfIcALauXQTA5xOesnq/x56bRIv2fW4777zje1G5uOHSuqflZnPpv35tuTeN2t0L9fVDsgy5pP/0JW5dh+A1/E3M2RnkHd9H9uYVt52Lvf46ZkarhV5NNTjrITpe4Yc/re8R4eUGilKyq9hXEhV+3WSiUwMNbeuqSUmHNXtNHDl/h3oiAMPJA6hc3HBu3hWVmxfmhBgyl8603K9G7Xbjb1EemYun49xhAO5Dx6BkZ2I4dfCO/MaI/x6VUszbSqelFa8bG8DTs+h1x2/mYJfWdtW7F9Rbt41zj/cq7TTsUmHeKn7XVb114D2qp+EkW6rXK+007NY28iDxbz9x68B7UMB7czner2Npp2G3Gss2kPzhqNJOw24+46bTd9T9uYjC8ulVyPp7SWmnYTfXlg+y6Uj2rQPvUe1ru5D03ojSTsMuvm/PZML8+/cq+qTHdKT+z/Yql/cDrzFf3TqolCS//2ypvK/PW3fubuv3smL3RHh7exdaWaIophvHMQshhBBCCCH+NYrdiNi0qWAyalRUFG+88QaPP/44zZvnjzHdsWMH33//PR9++KHjsxRCCCGEEOJmZGL1XVXsRkTbtgV3C508eTKff/45Dz30kKWsd+/e1K5dm1mzZjFs2DDHZimEEEIIIYS4Z9h1V44dO3bQqFGjQuWNGjVi9+7dt52UEEIIIYQQ4t5lVyOiXLlyzJgxo1D5zJkzKVeu3G0nJYQQQgghREmo1OpSefxX2bXE6xdffMGDDz7I2rVradasGQA7d+7kzJkzLF269Ba1hRBCCCGEEPczu5pPPXr04PTp0/Tp04ekpCQSExPp06cPp0+fpkePHo7OUQghhBBCiJtSqVWl8vivsqsnAuD8+fNERUURExPD4sWLCQ0N5YcffiAiIoJWrVo5MkchhBBCCCHEPcSunoglS5bQtWtXXF1dOXDgALm5uQCkp6fzwQcfODRBIYQQQgghbkmlLp3Hf5Rde/7ee+8xY8YMZs+ejU6ns5S3aNGC/fv3Oyw5IYQQQgghxL3HrkbEyZMnadOmTaFyT09PUlJSbjcnIYQQQgghxD3MrjkRwcHBnDlzhvDwcKvyv/76iwoVKjgiLyGEEEIIIYrtvzzJuTTY1RMxYsQIXnrpJXbt2oVKpeLKlSv89NNPjBkzhlGjRjk6RyGEEEIIIcQ9xK6eiNdee43U1FTat29PTk4Obdq0Qa/XM2bMGJ5//nlH5yiEEEIIIcTN/Ydv/FYa7F7i9f333+ett97i+PHjmM1matSogbu7uyNzE0IIIYQQQtyD7G5EALi6utKoUSNH5SKEEEIIIYS4D9xWI0IIIYQQQoh7gUolE6vvJhk8JoQQQgghhCgRlaIoSmknIYQQQgghxO1Inzq2VN7X44VPS+V9S9s9NZzp8vMDSzsFu5X9ehGbKtct7TTs0v70IbZUr1faaditbeRBftdVLe007NbTcJK4cY+Vdhp2CfxwPtEvDS7tNOwW+tUCcn75uLTTsJvzQ6/zwpdppZ2GXaa+7EnWll9LOw27ubYdwsHT8aWdht3qVQ4gY/obpZ2GXdxHfcS7vxhLOw27jX9IS9bWhaWdht1c2wwq7RTEPUKGMwkhhBBCCCFK5J7qiRBCCCGEEMIecsfqu0t6IoQQQgghhBAlIj0RQgghhBDi/qeSa+N3k3zaQgghhBBCiBKRngghhBBCCHH/kzkRd5X0RAghhBBCCCFKRBoRQgghhBBCiBKR4UxCCCGEEOK+p5KJ1XeVfNpCCCGEEEKIEpGeCCGEEEIIcf+TidV3lfRECCGEEEIIIUrErp6IiIgIVKqiW3vnzp2zOyEhhBBCCCHEvc2uRsTLL79s9dxgMHDgwAHWrFnD2LFjHZGXEEIIIYQQxaZSywCbu8muRsRLL71ks3zatGns3bv3thISQgghhBBC3Nsc2mTr3r07S5YsceQmhRBCCCGEuDWVqnQe/1EOXZ1p8eLF+Pr6OnKTNrm17oJHxz5ovLwxxFwmZclc8s6esBnrM/Q53Jq1K1RuiLnE1fdHA+BctwmeXfuj9Q8CjQZjfCwZG34ja8/WO5J/yMODKP/U4zgF+pN1+iyn3/+E1L0HiowPfWQwoY8OwTk0hNwrsUR9M5ury1fZjA3s2Y2aX35M/PqNHB31iuNzf2gQZZ8chj7An8wzZzn74aek7is695CHBxPy8OD83GNiuTjzW66usJ17QI+u1PjsYxL+3MSxFxyfe0n4tmpEhVeH49WgFs4hgex9cBRXV24o1ZwAXJp1xLV1D9QeXhjjoslY9ROGqFNFV9BocevYF+d6LVB7eGFOTSJz02/k7Cs4tl1adsWlaQc03n6YM9PJPbqHjLWLwGhweP5urbrg3uEBNJ7eGGIvk7r0e/LO2f7uej/8LG5N2xUqN8RcIu6jMQBog8ri2WMQurIRaP0CSVn6PZlbVjs8b4AFuyOZt/0ICenZVAz05rVuTWkQFnTLegcuXmX43NVUCvRh4bN9LeXD565m74XYQvGtK5fl60e6ODJ1i+7N9LSspcPFWcWFWBMLN+YQm2QuMr5pDR1Du7gUKn9lahpGU8FzLzcVfVrpqRGuRadVEZdi5uf12VyKK3rbJbVw826+X/s3CakZVAwJYMzg7jSoHGYz9sDpC3y1dD1RsQnk5BkI9vXmwTYNGdq5hSVmw/7jzPljG5fikjCaTJQP9OPRzi3o1bzubeeqKAqLf/6ODWtXkpGRTuUqNXjy2dGUC6tw03q7/t7Mgh+/5WpMNGWCQxny6NM0adHW8vrxowf5bcnPnD97kuSkRMa89QGNm7cptJ3Ll6L4ee43HD96EEUxU7Z8BK+8Phn/wFsfr7YsPHSOH/afJiEzhwp+noxpU5v6of42Y/dejmfEkr8KlS9+tBMR/2fvrsObuv4Hjr9jTZukqRvSAgWKFJfi7m5DNhjbmDJnbN8xY8yYs7GNIRN0DIcxGO5uxa1YS0vdNf77I5ASmkIJgY7fzut58jzk5Jybzz3c3N5zj1xfT9v7XJ2en/acZsuFa+TqDFTQqni9bT3aVHUuxjtpFymlcbgEdzdISId1h0yk5pStbN1QCYNayzgXb2bxTsfHdOs6Ejo1kLH/nJkNR1x43G/dz5z1u64f94HW475mFYd5o2Ni+X7ZBq4kpVqPez9vBrdrZnfcL99xiL/3HuXCtWQAaodV4OWBXYmsWsllMQv/HU41Iho1amQ3sdpisZCUlERqairTpk1zWXCOeDRuhffgJ8lcNAv9pXOo23TFf+y7JH/yOqbMtBL5s5b+TvaqBbb3EpmUwAlfUxi9tzj+gjxy1i3HmJyAxWTEI7IJPiPHYsrLRnfmmEvjD+zVnRrvvsX5Dz8l+8hRKgwfQv1fpnGg50B0iSUvKCo8+gjVxr/CuXc/IufESbT16xHxyQcYc3JJ37LdLq+yQgjhb48j6+Bhl8Z8Q0DPboS//SYxH39GzpGjhAwbQr0ZP3Gw7yCHsYcMf4Sqr7/M+Q8+IvfEKTzrR1Lzow8wZueQvs2+gaasEEL4m+PIOnR/Yr9bMrWKnOPniJ+znCZLfizvcABQ1otC0/sxclfNwRAbg0dUR7yeGE/GlAmYs9MdlvF69CWkGi05y37FlJ6MVKOFm8aMKhu2RNP9EXKW/YohNgZ5QDCeQ54BIG/NHy6N36NRS7wGjiZrya/oL59D3aoLfs9PIGXyOEyZJePPXj6bnNU3xSCVEfS/Lyk8us+WJHFTYkxLpjB6H14DH3dpvDdbd/ISX67bz7u9W9IwNIilh84ydv4GVrw4iBBvTanlcov0vLdiB82rVSAjr9Dus2+HdcZgKr4SzyrUMfTnlXStU/W+7EOXpm50bOTGgg2FpGSZ6d5cyUuDVHw8Jw/dbdqLhToLH8/Js0u7uQHhoYTXh6mJuWrk55UF5BZa8PeSUqizuCz29QdP8tWidUx4tDcNq4eybMchXpo6n2UfvkiIn3eJ/B5KN4Z1jKJmpSA83BREX4jjk/mr8VC6MbhdUwC81B483asdVYL9Uchk7Dxxjg/nrMRXq6ZV3er3FO9fyxawZuUiXnj9XUIqVGb5ojl8+v7rTJm+EA+VymGZ82dO8t0XExk68mmat2zHgb07+O6LD5j05TRqRNQFQFdUSFi16nTo2ptvP3vX4XaSEhOY+NZYOnbtwyOPjUGlVpNwNRaFm9KpfdlwPp5vdhzn7Y4NaVjBl2UnrvDyqj0sGdmFEK3jfQFY/ngX1G4K23sfj+LvN5jMjF2+Gx+Vki97RxGk8SApt8Auvyu1qi2hRS0Jf+0zk55roW1dKY91lDFtjQm98fZlvVTQpZGU2JTSj+cQX2gULiU503XHPMD6gyf4atE/THisj/W4336Il6bOY9mkl0s57hXFx73SjegLsXwy7y88lAoGt2sGwKFzl+nRvB4NwnvjppAzZ/0uXpgyh2WTXibQR+vS+MuFmBPxQDlV2wMGDKB///6216BBg5g4cSInT57k2WefdXWMdjw79SF/7xYK9m7BmJxA9rLZmDLTULd1fOfOUlSAOTfL9lKEhiP1UJO/d6stjy7mNEXHD2BMTsCUlkzetrUYrsWirFbL5fFXfmoUiUtXkLhkBQUXL3Ph06/QJSVR8dGhDvMH9+/DtT+XkrJ2PUVXE0hZs47EpSsIfeZJ+4xSKXW+mcyV73+m8Gq8y+MGqDR6FEnLV5C0dAUFly5zcfJXFCUlUWH4Iw7zB/XrQ+KiZaT+s4Gi+ARS164nadlKKj9dMvbaX37GlR9/puhqwn2J/W6lrt/B+YnfkbRyY3mHYqNq24PCQ9spOrQdU+o18v5egDk7A48WnRzmd6tZD0XVCLJmf4Ph4inMWWkY4y9hjLtgy6MIrY4hNgbdsb2Ys9LQx5xEd2wf8oquv5DVdOhN/r4tFOy7/ttdMQdTZjrq1qX9dgsx52bbXm6h1ZB4qCnYv82WxxB3kZy/FlAYvQfLfeg5uWHe3pMMbFyTQU0iqBbgzVs9WxDspWbxIce9KDd8vHo3PetVo0GlgBKfeamU+HuqbK99F6/hrpDTtW6V+7IPHRq5seGgjmMXjSSmm5m/oRCFQkLTWre/cLMAuQUWu9fNujZVkpVrZsHGImKTzWTkWDh/1URatusuqOZv3MOANo0Y1LYJ1UICeHNYT4J9tCzZftBh/lqhIfRsXo/wCoFU8Pehd4sGtKpbneiYWFuephFV6dSoNtVCAqgc6MujnVtSo2IQ0RdiHW6zrCwWC2tXLWHgsMeJatWe0CrVeHHcu+h0OnZt31BqubV/LaZ+o6YMHDqKipXDGDh0FJENmrB21WJbnkZNWzJ81LNE3dQ7cas/586kUdOWjHxqLFXDaxIUXJHGzVrh5e3j1P7MP3KB/nWrMDCyClV9tYxvX58gjQdLT1y+bTlflRJ/tbvtJbtp/f5Vp2LJ1hn4pk8LGlbwI0SrolFFf2oGeDkV4500j5Cy65SZs/EWUrNh1T4zCjlEht1+GIpEAgNaydh+wkxWnuPjWSGHgS1lrDlgplDv2ritx31jBrVtSrWQQN4c3uv6cX/AYf5aoRXoGVWf8IpB14/7hiWO+8+eeYShHaOICA2hakgA7z/eH4vFwv4zF10bvHBH06ZNo2rVqri7u9OkSRN27txZat5t27YhkUhKvM6etf8btGzZMurUqYNSqaROnTqsWLHivu6DUz0REydOdHUcZSOTo6hcjZwNK+2Si84cR1k1okybULfshO7cCYe9Fjcoa0YiD6yA7uKCUvM4Q6KQo6lbm9gZv9mlZ+zai1djx13oUjc3zDr7M5O5SIe2fiQSuRyL0XobpcpLz2HIyCRx6Qq8mjVyadw3YvesW5u4X+xjz9y9D22j0mJXYNbrbom9CM969rGHjX0OQ2YmSctW4tWksctj/39BJkNeoQr52+yHguljTqAIreGwiFvtxhgTrqBq1xv3Rq2wGPToTx8hb+My21Alw5XzuDdshbxSNYzxl5D6BOAW0YCiIyWHI9xr/IrK1cjdvMouWXfuGG5Va5ZpE6oWndCdv/1v934wGE2cuZbOU23q26W3DK/IsasppZZbGX2e+MxcPhvUnlk7jt7xe1ZEn6dHZFVU9+FurJ9WgpdaytnY4tuuRhNciDdSNUTG7hOlN8CUCpj0lAaJBBJSTazZqyM+tXi4RmQ1OWdjjTzVy4PqlWRk5VnYdVzPnpOuadQZjEbOxCXyZM+2dukt6oRz7OLVMm3jbFwixy5eZWx/xw1ui8XCgbOXuZKcxqs1ut5TvCnJ18jKTKd+o+a2NIXCjTqRDTl/5iRdew5wWO782ZP07j/MLq1B4yi7RsSdmM1mog/tod+gx/j0/XFcuXSewKAQBjwyyuGwpzsxmMycTcniiab2v9EWYUEcT3Tc+3nDo39sRWc0Uc1Xy5jmETSrXNyQ3nEpkfrBvnyx7RjbLybio3KjR83KjG5a066x4QreavD0kHApqbgRYDJDbIqFSgESjlwsvbHbrq6UgiILRy9ZCA1wHFfPplJirlm4nGyhTV3XxW0wGjkTe40ne9xy3NetfhfH/TXrcT+gc6l5ivQGjCYTXurSe5UE11u0aBGvvfYa06ZNo3Xr1syYMYOePXty+vRpQkNDSy137tw5tNriHqOAgOLf1d69exk2bBgff/wxAwcOZMWKFQwdOpRdu3YRFRV1X/ajzI2InJwyDh4Eux10JanGE4lMhjk3yy7dnJuFVOt95/Jab9zrNCJj9vclPpO4qwj5dAYSuRzMZjIX/YLu7HEXRW6l8PFBKpejT7M/+RrS0nHzdzy+NGPnHkIeGUjqxi3knTqDZ2QdQoYMQOqmQOHjjT41Da/GDQl5ZCCH+jnuzXBJ7N4+SORyDGkZ9rGnlx575q69BA8ZSNqmreSdPoOmbh2CB9nHrm3UkJDBAzg0cJjDbQhWUtX1Yz8v2y7dnJeD1NPx3TuZbwCKsBpYjAay509Fqtbg2X80EpWG3GW/AKA7vh+pWovPc++BBCQyOQX7NlOw3fG8FafjV2ut8efYx2/KzUbp6X3n8lpv3Gs3JHPuVJfGVRaZBTpMFgt+avu5AX5qD9LyChyWiU3P5vtNh/j9yd7IZXfu8D0Rn8qFlEw+7NfGJTHfSqu2xpBzSy9CboEFX23p8SVnmJm/oYjENBPubhLaN3Lj9aFqPl+QT2qWtSHh7yWlTX03th7Rs+GgjrBgGYM7uGM0wYEz996QyMwrwGQ246tV26X7aTWk5+SVUsqq+1vfkJmXj8lk5rm+HRjUtond57kFRXT/3zcYDEakUikTHu1Nizrh9xRvVqb1HOnlbT8/0Mvbh9SU5NuWu7W3wMvbx7a9ssjJzqSosJBVS+czbNQzPPbkCxw9vI9vPnuXDz6bSp16d3eDKavw+rGvsh8K5eehJD1f57CMv9qddzs3pHagDwaTiTVnrvLC8l3MHNKWxtfnUcTn5JMYn0rPiMpM7d+SuKx8vth2FKPFwrNRrh0BoLn+s80rsk/PLwIvdcn8N1Tyh4bhEmb+Yyo1T91QCSE+En5ZX3oeZxUf9/bDJf08NaRn5962bPc3vyo+7vt1ZFDbpqXmnbpsA4HeWqLq3H6+zkPjIZnk/O233zJmzBiefvppAL777jvWr1/Pzz//zOTJk0stFxgYiLe3t8PPvvvuO7p27cqECRMAmDBhAtu3b+e7775j4cKFLt8HuItGhLe3920fMHczk+n2PyidTodOZ38CUiqdG68JWA8ay527ztUtOmAuzKfweMkucIuukOTJbyJVuqOMiMR70GhM6cnoYk47H1dpbo1VIsFSSvxXfpqJW4A/TZbMA4kEQ1oGicv/IuzZJ7GYzMjUKmp//Rnn3p2EITPL9bGWUDL20uo+9ueZKPz9aPTnXCQSCfr0DJJW/kXo009iMZmQqVTU+vJTzn/wEcasrPsf+v9XpdT/jd9rzp8/Y9FZx+PnrVmI9tGXyF01B4wGFFVroerY1zrP4upFZH5BePYdiTk3i4Itqxxu9x6DvTVKB2klqZpf/+2ecDx85UG49fxnwYKEkudEk9nMhGXbeaFDY6r4l214xoro81QP9KGeg2FPzmgaIWd45+JGz/RVBTeCtiPh9qfOK0kmriQVn88vXSvkrcfUtGugYNl26zlcIoG4ZBOr91jfx6eaCfaV0qa+wiWNiOJYb6l/i+P6v9lvbz1FQZGeE5evMnX5JioH+tGzeT3b52p3N/58/3kKdXr2n7nEN0vWUynAh6YRZR/Ot3PrBmb99JXt/dsTv7TGe0toFsudr29KHGOWkmm3YzZb/zObtmhD7wHWGzNVqtXg/JmTbPxn5V03Iorjsn9vAUqr+io+nlTxKZ5AXT/Ej+S8QuYdjrE1IiwWCz4eSt7t3AiZVELtIB9S8wuZezjmnhsRkWESejcrbhgv3G66KWgH++GAmxwGtJTx922GKGlV0K2JlD+2mjC5bh51CSXr3nLHY+K3t56mQKfjxKV4pi7fQOUAP3pG1S+Rb/a6naw7cIJZbz6FUnF/5qMIJen1eg4fPszbb79tl96tWzf27Nlz27KNGjWiqKiIOnXq8N5779GxY0fbZ3v37uX11+0XpenevTvfffedy2K/VZkbEVu3Fs8huHLlCm+//TZPPPEELVu2BKzBz5kz57YtqBsmT57MpEmT7NImTpzI03coZ87LxWIyIb3lzqVU44U5N9txoZuoWnSi4MAOMDmYSWWxYEpLwgQYEq6gCK6EZ7eBLm1EGDIzMRuNuAXY37lX+PliSHfcNWzW6Tg7YSLn3v8YN39fdClpVBg+GGNeHobMTDS1auJRuSL1ZhTfob3xsJX2Zw6zv3t/iuLufY6EISsTi9GIwt/PPnZfX/S3if38ex8S8+EnKPx80aemETL0RuxZqCNq4lGpIpHTbuoZuh57uxOHONBrAEX3aX7Hw8ZccP3Y19hflEo1Wsx5jnsJTbnZSHMybQ0IAGPKNSRSKTIvX0zpyai7DqYoeg9Fh6yT9E3J8eS5KdEOfJKCrX+VqXFepvjzc6zx39JjKPPUlum3q27RgcJDO+EONyjuBx+VEplEUqLXISO/CD9NyZWL8nUGTl1L42xiOp+vtS7gYLZYsACNJ/3Oz6O6E1Wtgi1/od7I+pOXGNvRdUP5TlwyciWp+C69XGa96NCqJXa9ERqVhNyCsl8BWYC4JBOBPjJbWk6+pcQKT8mZZhrWcM1FiY9GhUwqLdHrkJGbX6J34lYV/a139mtUCiI9J58Zq7faNSKkUimhgdZzWkTlEC4npfHbPzvvqhHRNKoNNSLq2N4bDNarzqzMDHx8i8/1OdmZJXonbubt41ui18FapuxzGbRaL2QyGRUrV7FLr1g5jLOnT5R5O7aYPK4f+7f0OmQU6kr0TtxOvWBf1p6Ns733V7sjl0rthi5V9fUkvUCHwWRGUYbeu9KcT7CQkF58npBf35TGw743Qu1u7Y1wxEcDPhoJw9sVx3Hjuv3dYdYJ2YFeEjTuEp7uXvxbkEolhAVCsxoyPltsuqfTp+24z3Z03Je+mANAxYAbx30w6Tl5zFi9pUQjYu76Xfy6dgfTxz1BzUr3Z0Ws8lBeD5sr7ea4oxvkaWlpmEwmgoKC7NKDgoJISiq5SA1ASEgIM2fOpEmTJuh0OubNm0fnzp3Ztm0b7dpZhyomJSXd1TZdocyNiPbtiydyffTRR3z77beMGDHCltavXz/q1avHzJkzGT169G23NWHCBMaNG2eXplQqSX1j5O2DMBkxXL2Ee636FB0vnljkXqv+He9QKmvUQREYQvreLbf/DhsJErlrW+YWg5G8U2fwbd2CtI3Fcfi2bkHapm23L2s0okuyjr8O6t2D9K07wGKh4OJlDvQabJe36usvIlerifnkS4erJjkbe+6pM/i0akn6puIGpU+rKNK33Dl2fbI19sBe3UnfttMa+6XLHOx3S+yvvIRMreLC5C/R3ccD/6FjMmG8dgW3GpHoTxevYOVWPRLdmSMOixiunMc9shkSNyWW63NTZP7BWMxmTNnWixWJmxtYbrmItJhd3yVsMmG4egllRH2KbuoJVEbUp+jE7R9Q6Va9DvKAEPL3bb1tvvtFIZdRu4If+y5eo3PtKrb0fRev0aFWybGrGqUbS18YaJe2+OAZDlxO5OuhnajoY38BsOHUZfRGM73r39swmpvpDKCzm9hsITvfTESonPhU60WuTArVK8n5a1cpV1KlqBgg41r6zb0TJoJ87P9wB3pLychxze1ZhVxO7dAQ9p2+SKdGtW3p+85cokODss2FA+vdb73x9o3QsuS5lYdKZbfiksViwdvHj+PRB6kabp1LYDQYOH3yKI8+8Xyp26lZK5Lj0QdtPQgAx6MPULN2ZJljkSsUhNeoTWKC/Zj5xISrBAQGlVKqdAqZlFqB3uyPS6FT9eKG7/64FNpXCynzds6lZuGvdre9bxDix7pz8ZgtFqTXzzWxmXn4q93vqQEBoDeC/pZRbrmFFqoGS0i6vnqSVAphgRI2H3V8jKblwPS19jcbO9aX4iaH9UfMZBdAfpGlRJ5+UTLScizsOWO+5/svCrmc2mEV2HfmIp0aFzdS952+SIeGZe+tsVDymJ6zfhe/rNnGT6+Opm6VivcWqACUfnP8ww8/LLVMyZ7H0nuZIiIiiIgoPt+1bNmSq1ev8vXXX9saEXe7TVdwamL13r17mT59eon0pk2b2sZ33U5prbOyyN3yN76Pv4w+7iL6y+dRt+6CzNef/J3WVS+0/R5F5uVL5jz7ZTlVLTuju3weY2LJCUme3Qagj7uEMTUJiVyOe93GqKLakfnnLKdivJ2rv82j9lefknPyNDnRx6gwbDDKkBASFi4BoNobr6AMCuTMW+8B4FElDG39SHKOnUDupaXyk6NQ16jOmbfeB8Cs15Mfc8HuO4y51vGSt6bfq/g586j1+afknTxFztHjhAwdjHtICNcWLQWg6usv4xYUyLm3378eeyie9SLJPX4SuVZLpSdGoq5RnbNvfwCARa+nIMZ+RYgbsd+a/qDJ1CrU1YsvEFVVK6FtUAt9RjZFVxPLJaaCnevQDn0OY/xlDHEX8GjeAam3H4X7rQ1SdfdHkGp9yF0yEwDdsb2oO/XHc8gz5G9ajlTliabXcIoO7bBNrNafOYpHmx4Yr8XahjOpuw5GdybaZb0QN+RtW4PPyJcwxF1EfyUGdavOyHz8yd9tXQFL22eE9be74Ce7cuoWHdFfiXH420UmQxFsXd9cIpcj8/JBUTEMs64IU1rp48/v1qiWkby7fAd1KvjToHIgyw6fIzE7j0eaWv+Yf7/pECk5+Xw6qD1SqYQaQfZ3j33V7ijlshLpYB3K1LFWKN4q9xKfudK2aD3dmitJzTKTmmWmWzMlBoOFQ2eLhxyN6uZOVr6F1butjc6eUW5cTjKRmmnGXSmhfUM3KgVIWbK1uHdra7SOcUPVdGvmxpHzBsKCZbSq58afmwpLxOCskV1b8d5vy6kTVoH64ZVZvuMQSRnZDGlvXbZy6vKNpGTl8slTgwBYtHU/wb7eVAm29gQcvRDHvA17GN6peHLhr//soG5YRSoF+GAwmth1MoY1e48x4bE+9xSrRCKhV/9HWLlkHiEVKhFcoTIrl8xFqVTSpn3xSmQ/fvMxvn4BtoZFz36P8OH/XmLV0vk0jWrLof07OXH0EJO+LF42vaiwgKTE4hXsUpITuXIpBo3G0/YMiL6DRvDdlxOpXbcBdes35ujh/Rw+sIeJk52bTzSycXXeX3+IOkHe1A/xZfmJKyTlFjCknrW35ofdp0jNK+Sj7tZx939EXyBEqyLcV4vBbGbt2atsvnCNr3oXTzQfUr8qi45d4uvtxxnWoBpxWfn8fvA8wxu6riF9swPnzLSpIyUj10xGroU2daQYjHAytvgc17+FlNxC2HLMjMkMqbd0kBZdH9Z0I13vII/eCIX6kunOGtm1Fe/9uszBcW+ty6nLN5CSmcMnY4YAN457L6oEW4dFHr0Qy7wNuxnesYVtm7PX7WTaqs189vQjVPD3Ju36/AqV0g2V+z0MK/+PK+3muCP+/v7IZLISPQQpKSklehJup0WLFsyfP9/2Pjg4+J63ebecakRUrlyZ6dOn880339ilz5gxg8qVK7sksNIUHtlDllqDtucQZFofDIlXSZv2mW3FFpnWB7mv/XAhibsKj4ZRZC/93eE2JW7ueA99Grm3HxaDHkNyAhlzfqDwyO3HpjkjZe165N5eVHnxWZSBAeSfv8DxZ15Ed816YeoW6I+yQnHXokQmpfKYx1FVDcNiNJK57yCHhz1OUcI1l8d2J6n/bEDh7U3Y2OdwC/AnP+YCJ55/qTj2gADcQ4rvTkmkMio9URx71v5DRI8Yje7ag4/9bnk1iaTl5nm293W+fgeAq3OXc3zMhHKJSXdiP3lqDerO/ZF6emNMjid79jeYs6zDyaSe3si8i4ebWfQ6sn77Es++o/B9cRLmgjx0Jw6Qt2GpLU/+1lVYsKDuZv09mfNz0Z2JJv+mPK5SGL0XqdoTz+6DkXlZf7vpMz63/XalWm9kPvbD5STuHrg3iCJ7+WyH25R5+RL41pe2956d++HZuR+6mFOk/fiRy2LvEVmN7AIdM7cfJTWvgOqBPvz0WDcqXH9GRFpuAUnZ+Xe93Stp2UTHJTN9VHeXxVqaTYf0KOQShnZyR6WUcCXJxE8rCuyeEeGjlWKh+O6sh1LCiM4eeKokFOktxKea+W5pAbHJxXniks3M+ruQfq2V9IhSkp5jZvn2Ig6du8MC/Hehe7NIsvMLmLlmO2nZuVSvEMgPLz9Ghetr5adl55GUUXzlZrZY+GHFJhLSMpFLpVQK8OXlQV0Y0q54gmmRzsBnf/xNSmYOSoWCKsH+fDJmMN2blf3Of2n6DX4MvU7Hrz9/S35eLtUj6vDOR1PseizSU5OR3jT0IqJ2PV5960MWzZ/Fovm/EBRckVf/95HtGREAF2PO8tE7r9jez/3lBwDad+7J2Netz41o3qo9z4wdz8ol8/l95ndUqBjKuHc+oVZd5x6i161mJbIK9czaf460giLC/bRM7d/K9oyItPwiknKLG4wGk5nvdp4kNa8QpVxGNT8t3/drafcQuWBPFT8NbMU3O04wfMEWAjQejGgYzuimZVup7W7tOWNBLrPQs6kUj+sPm1uwzf4ZEVpV6XMTy0v3ZvXIzitg5t/brh/3Qfzwyqji4z7rluPebOGH5Rutx73sxnHfze64X7ztAAajiTen/2n3Xc/17cjz/RyvXvZQkZTPcKa7uTnu5uZGkyZN2LhxIwMHFvdab9y4kf79+5f5O6Ojowm56ZqrZcuWbNy40W5exIYNG2jVqpWj4i4hsTjxq1m7di2DBw8mPDycFi2sLdx9+/Zx4cIFli9fTq9evZwKJv4lx88beBhU+nEJW2vc+5NOy0PHmGNsr92wvMNwWvszR1mjKPuwhn+b3oZzpEy4fw9Ku58CJ88l4dWHd2Wtit8vomjhF+UdhtPcR/yPl78r+8p5/yY/vKalYPufd874L6VqP5yjManlHYbTGtYIIG/a23fO+C+kGfs5Hy90XSP1QXt/hJyCHWVfuvffRtXu/q0Eea8K539WLt/rMfKdu8q/aNEiRo0axfTp02nZsiUzZ85k1qxZnDp1irCwMCZMmEBCQgJz584FrCsvValShbp166LX65k/fz6ff/45y5YtY9Agaw/snj17aNeuHZ9++in9+/dn1apVvPfee/+OJV5v1qtXL2JiYvj55585c+YMFouF/v378/zzz9/3nghBEARBEARBKMHFzxm5X4YNG0Z6ejofffQRiYmJREZGsnbtWsLCwgBITEwkLq54MQK9Xs/48eNJSEjAw8ODunXrsmbNGrub9q1ateLPP//kvffe4/333yc8PJxFixbdtwYEONmIALh8+TJXrlwhMTGRpUuXUrFiRebNm0fVqlVp0+b+rHUuCIIgCIIgCA+7sWPHMnbsWIefzZ492+79W2+9xVtvvXXHbQ4ZMoQhQ4a4IrwycWrw2LJly+jevTsqlYro6Gjbsla5ubl89ln5dCUJgiAIgiAI/10SibRcXv9VTu35J598wvTp05k1axaKmx5Q0qpVK44ccbzcpCAIgiAIgiAI/z841Yg4d+6c3bq0N2i1WrLEk4cFQRAEQRAE4f81pxoRISEhXLhQ8hkEu3btolq1avcclCAIgiAIgiDcFamkfF7/UU41Ip577jleffVV9u/fj0Qi4dq1ayxYsIDx48eXOklEEARBEARBEIT/H5xanemtt94iOzubjh07UlRURLt27VAqlYwfP56XXnrJ1TEKgiAIgiAIwu39hyc5lwenl3j99NNPeffddzl9+jRms5k6deqg0WhcGZsgCIIgCIIgCP9CTjciAFQqFU2bNr1zRkEQBEEQBEEQ/t+4p0aEIAiCIAiCIPwrSP67k5zLgxg8JgiCIAiCIAjCXRE9EYIgCIIgCMLDTyrujT9IorYFQRAEQRAEQbgroidCEARBEARBePiJJV4fKFHbgiAIgiAIgiDcFdGIEARBEARBEAThrojhTIIgCIIgCMLDTyqWeH2QRE+EIAiCIAiCIAh3RWKxWCzlHYQgCIIgCIIg3IuiVT+Wy/e693+pXL63vP2rhjNlf/VyeYfgNK83f3ho4/d68wdS33uyvMNwWsAnv5My4fHyDsNpgZPnskYRUd5hOKW34RznR/Qo7zCcVnPhOnIPrSvvMJzm2bQHT01KKe8wnPLbxEB2n84r7zCc1rqOhi+Xmcs7DKe9NVhKVvSW8g7DKd6NOjFrU3lH4bxnukDqqf3lHYbTAupGlXcIwr+EGM4kCIIgCIIgCMJd+Vf1RAiCIAiCIAiCUyRiYvWD5HQj4sCBA2zbto2UlBTMZvsu3W+//faeAxMEQRAEQRAE4d/JqUbEZ599xnvvvUdERARBQUFIbmr5SUQrUBAEQRAEQXjQpGKU/oPkVCPi+++/57fffuOJJ55wcTiCIAiCIAiCIPzbOdWIkEqltG7d2tWxCIIgCIIgCIJzxGiYB8qpfp/XX3+dn376ydWxCIIgCIIgCILwEHCqJ2L8+PH07t2b8PBw6tSpg0KhsPt8+fLlLglOEARBEARBEIR/H6caES+//DJbt26lY8eO+Pn5icnUgiAIgiAIQvmSiInVD5JTjYi5c+eybNkyevfu7ep4BEEQBEEQBEH4l3OqEeHr60t4eLirYxEEQRAEQRAE54glXh8op2r7ww8/ZOLEiRQUFLg6HkEQBEEQBEEQ/uWc6omYOnUqFy9eJCgoiCpVqpSYWH3kyBGXBCcIgiAIgiAIwr+PU42IAQMGuDgMQRAEQRAEQbgHYqGfB8qpRsTEiRPLlG/hwoX069cPtVrtzNcIgiAIgiAIgvAvdF9noDz33HMkJyffz68QBEEQBEEQBOsSr+Xx+o9yqieirCwWy33ZrlvDtiibdUai0WJOS6Rwy3JMCRdLLyCTo2zZA7c6zZCoPTHnZaHbuwHDyX0ASP2CcW/TG1lQZaRefhRuWYb+8Lb7EvvDHr97846o2vZEqvHGmJJA/to/MMTG3DZ2Vcd+uDdsiVTjhTknk4Jtf1N0ZCcAXmP+h1vVWiWK6c4dI2fedy6P36NFZ1RteyH19MKYkkDe3wswXDl/2/jVnQfg3rAVUk8vzNkZ5G9dTdHhHcXbbN0dj6hOyLz9MOfnojt5kLz1S8BocHn8ZeXbpinV3hiDV+NI3CsEcmjwWJL/2lxu8ZTGq2sffPsMQebtiz4+ltS50yk8d6rU/J6tO+Lb9xEUwRUwFxSQf+wQqQtmYc7Lve+xLtm4k3lrtpCWlUO1isG8MWoQjWo5XqVuy8FjLN20i/OxCRgMRqpVCuHZwT1oWb+2Xb7c/AKmLV7DlkPHyc0voEKAH6891p82Devel33o315N+ybuqNylXEowMH9tLtdSTbct46GUMLizmsa1lKg9pKRmmli0IY8TF/QA9GqjokktJSH+MvRGuHDVwNJNeSSl3367t2OxWFi1aCbbNyynID+XajUiGfns/6gYevtVAQ/t3cyKP34mNSmegOBKDHpsLE1adLJ9bjIZWfXnTPbt+IfsrHS8fPxp07EPfR55GqmDVV3m/Pwp2zcsZ/hTb9Ct76NO78+NfYre/BPnDi5GV5hDQOX6tOr3Pj5BNcpU/uKxNWxbNJ7Q2p3pOupHW3ri5YOc2Pkb6QmnKMhNpfPIH6hSp8s9xXqrpRu2M3/1RtKzsqlaKYTXH3+ERrUdx731QDTLN+7g/JV49Ebrsf/MkD60aFDHLs/sleuIT0rFaDJROTiQR3t3oVe7KJfGfYPFYmHP2h85vnsRuoIcgqs0oMvQD/CvUHrdnz+6gf3rp5OVGofJZMQnIIymnZ+kbtQAW57962dw/ugGMpIvIVe4U7FaI9oNGI9vUDWXxb78n00sXLWW9MxsqlSuyKtPPUaDOhEO86ZlZPHjnD84d/EK8YnJDOnVlVfHjCyRb/HqdaxYv4XktHS8PT3p0LIZz418BKWbm8viFv4b7msj4n5QRDTGvdMgCjcuxpRwCbcGrVEPeYHc3z7FkpvpsIyq75NI1J4UrP8Dc2YqUpWn3TJgEoUb5qw0DOeice84SMRfCmVkczS9HiVv9TwMcTG4N+uA1+PjyJj6LubsDIdltMPHItVoyV3xO6b0ZKRqLUhlts9z/vgRZMXvpSoNPi9+hO7kQdfHXy8KTe/HyF01B0NsDB5RHfF6YjwZUyZgzk53WMbr0ZeQarTkLPvVGr9Ga1f3yoYt0XR/hJxlv2KIjUEeEIznkGcAyFvzh8v3oaxkahU5x88RP2c5TZb8eOcC5UDToh2Bjz9H8m8/UXTuFF5delHx7U+4Mv5ZjOmpJfK7R9QleOx4UufOJO/IPuS+/gSNeZngZ1/j2rcf39dYN+w9wjfzVvD2k4/QoGZVlm/ZwytfTmfJlxMI9vctkT/67EWiImvx4tA+eKo9WL19P69/PYvZH42jVpVKABiMRl78fBo+Wk++eOVJgny9Sc7IROXufl/2oWdrFd1aevDrylyS0430aadm/Chv3vkxgyK94xs+MimMH+VNTr6ZaUtyyMwx4auV2eWPCFOw5WAhl68ZkEklDOqkZtxIb96blo7eyXb0PyvmsOGvBYx5+UOCKoTy99Jf+frDsXz203I8PBwPj71w9jjTv57AwEefp3FUR47s38r0r9/m7c9+JbxmPQDWLp/DtvVLGfPKJCqGhnPlwml+/WESHioNXW9pJBzZv5VL50/i7Rvg3E7c4viOXzi5ezbtBn+G1r8KR7dOZ91vYxg87h/clLcf8pubmcCBf74iqEqTEp8Z9YX4BkdQs/FANv/xqktivdnGPYeYMmcJb40ZTv2IcFZs2snrn//En9984PjYPxND83q1eWF4fzQqFX9v28MbX07jt0/+R0TVygBo1WqeHNCTsIpBKGRydh05wSfT5+Lr5WnX2HCVAxtncXjL7/QY9Tk+gVXYt+5nlvz4JGM+WIebu8ZhGXeVFy26v4BvcDVkMgUXT25l3fx3UHn6UbVOWwCuxhygUbvHCA6rh9lsYtfqKSz5YQxPvr8GN6XqnuPevGsfU39fwBvPjKZe7RqsWr+V8Z98zbzvJxMc4F8iv8FowFur5fHB/Vj89zqH29ywfQ/T5y/h7RfHUK9WDa5eS+LTH2YB8MpTj91zzOVOzIl4oB66Phi3ph3Rn9iL4cRezBnJFG1djjk3E7eGbRzml1epjbxydQqWTccUew5LTgampFhM1y7b8piS4ijavgrD2SNgMor4S+HRuhtFh3dQdHgHptRE8tcuxJSdgUfzTg7zK2pEoqgSQfbcKRgunsaclY4x4TLGqxdseSyF+Vjycmwvt/C6WAz6+9KIULXtQeGh7RQd2o4p9Rp5fy/AnJ2BRwvH8bvVrIeiagRZs7/BcPEU5qw0jPGXMMYVx68IrY4hNgbdsb2Ys9LQx5xEd2wf8opVXR7/3Uhdv4PzE78jaeXGco3jdnx6DyJ763pytq5Df+0qqXNnYEhPxbtrH4f5ParXwpCaTNb6VRhTkyk6d4rszWtRVqt532Nd8M82+ndowYCOLal6vRciyM+HpZt2O8z/xqhBjO7bmbrhYYQGB/LisL6EBgew88hJW55V2/aRnVfAN68/TcOIaoQE+NIwIpyaYRXvyz50jfLg750FHDmrIyHVxK8rc3BTSIiqpyy1TNtG7qg9pPy4KJsLVw2kZ5uJuWrganLxeWbKgmx2HyviWqqJq8lGfluVg7+3jCohilK3ezsWi4WNf/9BnyFP0aRlJyqFVWfMK5PQ64rYv8PxhRHAxr//oE6DKHoPfoqQSlXpPfgpatdvzsbVC215Lp47TsPmHWjQtC3+gRVo2qoLkQ1bcOXiGbttZaansGDWlzz7+ifIZPd+r81isXBqz1wadHiOKpHd8A2uSftHPsdoKOLS0b9vW9ZsNrF98Vs07vISWt/KJT6vHNGOpt1eo0pkt3uO05GFazbTr2Mr+ndqQ9WKIYwbPZQgPx+WbdzhMP+40UMZ1a8bdcKrEBoSyNgRA6gcEsjOI8dteZrUrUmH5g2pWjGESsEBDO/VieqhFTl69oLDbd4Li8XCka1zier+PDUbdiOgQk16jvoCo76IMwdLr/vQmlHUaNgVv+BwvANCadJxNAEVI0i4eNiWZ8hLvxLZchD+FWoQWKkWPUZOJjfzGslxpfem3o0/V6+jT+f29O3agSqVKvLqmJEE+vmycv0Wh/lDAgN4bcxIenZsg1rluBFz8vwF6tWqQbd2rQgJDKB5w3p0adOCsxcvO8wvCLfzcDUipDJkwZUxXjlrl2y8crbUizZ59XqYkq/i1rwLns9/jGbM+7h3GABy5/7A3ZOHOX6ZDHmFKugv2J8c9RdOIS9liIGyViOM1y6jatsT37e+xee1yah7DLtt7O5N2qE7sR8MepeGb4s/5qRdsj7mBIpQx13abrUbY0y4gqpdb/ze/g7fN75E03O4XfyGK+eRV6yCvJK1+1rqE4BbRAP05465Nv7/b2Ry3KvWoOC4/XLQBceP4F6ztsMihedPI/f1R92wmXUTXt5ootqQH33gvoZqMBo5e/kqLerZDyFoUS+C4zFl+8NrNpvJLypCqyn+w77jyEnq16jCF7OX0O2Fdxn6v8n8tmoDJrPZpfEDBHhL8faUcepi8e/KaIJzVwxUr1T677FhhJKL8QZG9vJkyhv+fPSCL73bqG57s89Daf2zkl/o3H6kJieQnZlO3YYtbGkKhRsRdZtw4Wzpv6uL544TeVMZgMiGLbl402+xRu2GnDl+gKSEWADiLp8n5sxR6jVpbctjNpuZ9d379Og/6o7Dp8oqNzOewtw0KtYo/h6Z3I3gqs1Ijou+bdmjW6bhrvYhoukQl8RyN6zHfhxR9e17B5rXr82J85fKtA2z2UxBYRFepSywYrFYOHjiLLGJyaUOkboX2enx5OekUqV28Y06ucKNStWbkXD59nV/c4yxZ/eSkXyZStWblZpPV2gdVumu9rq3oAGDwcj5i1do1iDSLr1Zw3qcPHubIcR3UL92Tc5dvMLpGOsQ6oSkFPYdOUbLJg3uKV7hv6lchjPpdDp0Op1dmlJZ+t2wGyQeaiRSGZZ8+/HPlvxcJGqtwzJSbz9kFathMRooWPkLEg81Hl2HInFXUbjuwQ43eZjjl6o8kchkmPNy7NIt+dlINZEOy8h8AlCE1sRiMJCz4Aekak80fUch8VCTt+K3EvnlFasiD65EroPPXBd/tl26OS8HqafjE77MNwBFWA0sRgPZ86ciVWvw7D8aiUpD7rJfANAd349UrcXnufdAAhKZnIJ9mynYfvu7i/91Mq0WiUyGMdt+CJ8pOxO5V8khEgBFMWdI+vFLQl6ZgEThhkQuJ+/QXlJmT7uvsWbl5mMym/H1sv+N+np5kpZdtrkY89dupUinp2tUI1taQko6h07H0KNVE75/63niklL5cvYSTCYzzwzq4dJ90GqsF/Y5efYX9jn5Zvy8Sr+XFOAjo3ZVGftOFPHdH1kE+coY2csTqRRW73D8sNFh3TWcj9WTcIe5FqXJybIOLdR6+9nvg7cv6amJpZbLzkpH621/7Gi9fcnOLB6q2GvQExQW5PHuy4ORSqWYzWYGPTaWFm2L6/ufFbORyWR06TPCqfgdKcxNA8BDYz8ExUPjR17WtVLLJcce4dyhZQx8eYXLYrkbWTl51499T7t0Py9P9mVll1LK3oI1myjU6encsrFdel5BIX1emIDeaEAmlfLmUyOIqu/4BsK9yM+xDo1Ue9ofT2qtPzkZpdc9WBsF099ph8moRyKV0mXYRKrUbu0wr8ViYdvyyVQMb0JAhXvvHc3OzbXWvbf93ydfLy3pZax7R7q0aUFWdg5j3/0EiwVMJhMDundi1KC+9xryv4N4YvUDdV8bEWFhYSUeRAcwefJkJk2aZJc2ceJEXi/zSrC3jN+VlEwq/kwCFgsFf88BfREAhVtXoOr/FIWbymvy68Mc/62B3uaWpNS6Y7lLZmLRFQKQ98+faIePJW/1vBKxuzdthzEpHmPCA+5WLWUBAMn12605f/5cHP+ahWgffYncVXPAaEBRtRaqjn2t8yyuXkTmF4Rn35GYc7Mo2LLqge3C/xvXj3dH3CqGEvjEC6Qv/4P844eRe/sS8NjTBI15heSZUx5IaDezWG579Nus23OYmcvX8c24p+0uxiwWCz5aDe8+PRyZVErtqpVJzcxm3pot99yIaFFPyeN9ir/ruz+sFx0lalZS+qkHrPuck29m9upcLBaITTTi7SmlRyuVw0bEyF4aKgfJmfyb4/ldjuzdvpa50z+zvX/t3e9vhGbPYrH9Jm8bsF0Ri13agV0b2Lv9H559/VMqhlYj7vJ5Fv76Dd4+AbTu1JcrF8+w8e8/mfjNgjt/121cOLqa3Ss/tL3v9vjPDvfJuviI4+/R6/LZtvgt2gz8CHe1j9OxuMKtdWFxkObI+t0H+WXpGr4a/3yJRrjKXcm8L96hsEjHwZPn+H7eUioG+tOk7r1dgJ8+8BcbFxYvQz9o7IwbO2G/D2VY+MVNqebxCSsx6AqIPbeXbcs/x8u/MqE1S04A37z4I1ITzjNinGtv7pU471C2ui/NkZNnmLtsNW88M5o6NcOJT0zm+9/mM3vxSp4YOuCeYhX+e+5rI+LkyZMO0ydMmMC4cePs0pRKJUVTx992e5bCfCxmU4m79hKVJ5aCHMdl8nKsd5+vX4ADmNOTkEikSDXemLNKTuC8Xx7m+M0FuVhMJqQa+7siErW2xN19W5ncbMw5mbYLcABT6jUkUikyL19M6Tct/6twQ1mvOQWbV96P8EuNX6rRluhdscWam430lviNKfbxq7sOpih6D0WHtlvLJMeT56ZEO/BJCrb+VeoF8X+dKScHi8mE3Mv+4kim9caY4/gC1Lf/MArPnSbz76UA6OMuk6wrIvTDb0hbPAdTluPJ/ffK21ONTColPcv+OMnMycXvlju0t9qw9wgfz1rIF688SVSk/XAof28tcpkM2U13zqpWCCI9KweD0YhC7vzp+eg5PZfii+vxxqa8NFKyb+qN0KqkJXonbpada8Zktj+ME9NMeHvKkEnBdFPRR3tqaFhTyeezM8nMLftQpobN21Pt+sRnAOP1oYzZWel2k5pzsjPRltJLBeDl7UdOpv0CCbnZmXjd1DuxeM739Br0BFFtuwNQKawG6amJrFn+O6079eX86WhyszN485netjJms4lFs6ewcfUffDWzbD2MobU7EVi5vu29yWjdp4K8NFTaQFt6UX4GHhq/EuUBctPjyMtMYOO8sbY0i8Var7+9F8mQ19ei9QstUzzO8tZqHB77Gdm5JRoFt9q45xCfzpjHZ689Q/N6JXsYpFIplYOtdVGzSmWuJCQyZ9W6e25EVK/fiZAqxUNzbtR9fk4aGq/iui/ITUelLTk5+WYSqRSfwDAAAivXJiP5Igc2zCzRiNi8+GMuHt/CsNfn4+kTfE/x3+Dl6Wmt+0z7v6+Z2Tl3rPvb+WXhMrq3b0Xfrh0ACA+rTJFOx5c//87jQ/o5XKXsYWIRE6sfKKf+SplMJqZMmcLixYuJi4tDr7cfv56Rcfs/5kql0uHwpSIHee2YTZiSriIPq4UxpniSljwsAsOFEw6LGBMuoYhoBAo32zh7qW8gFrMZc17Wnb7RtR7m+E0mjNeu4Fa9LvozxePY3arXQX/mqMMihtgYlHWbgpsS9NbhazK/YCxmM6ZbVnNSRjZDIlNQdHTP/Y2/RiT608UT49yqR6I7c8RhEcOV87hHNkPipsRyI35/+/glbm5gueWCyWIWK0TciclI0eUYVPUbkXeo+P9cVa8R+Yf3OSwicVOC+ZYhMjfmD9zH6lbI5dSqWpn9J8/RsVnxxcn+E+do36ReqeXW7TnMxzMX8ulLj9OmUcklWxvUrMq6PUcwm822P9xxSSn4e2vvqQEBUKS3UKS3r6usXBN1qrkRl2SdFC2TQkQVBUs25ZW6nZirBlrUc7frLA3yk5GVa7JrQDzWU0PjWkq+mJNFWtbdzYXw8FDbrbhksVjw8vHj9LH9hFWzLv9sNBg4d+owjzz+SqnbCY+oz6lj++nWr3iFmZNH9xEeUfx/ptcVIZXaHyxSqRSL2bp3rdr3ok795naff/vRS7Rs34s2nfuVeZ/clGq7FZcsFgsenv5cu7AH/wrW+QUmo56kywdp1v0Nh9vwCqjGwFfsezMPb5yKQZdPiz4TUHu55mL1dqzHfigHTpyhQ/OGtvQDJ87QrmnpY+jX7z7Ip9Pn8fErT9Gmcem/kZtZLNZ5APfKzV1jt+KSxWJBrQ0g9uxugioX1338hYO063/7G5clY7RgNOrt3m9e/DEXjm1k2Gvz8PYvOfHdWQqFnJrhVTh47CTtWzS1pR86dpI2zRvfpuTtFen0SG55roFUKsWCRdzzEu6aU03OSZMm8e233zJ06FCys7MZN24cgwYNQiqV8uGHH7o4RHv6Q1txq98SRWQLpL5BuHcchFTri/7YLgCUbfvi0WuULb/hzCEshfmoeo5E6heMrFI47u0HYDixr3g4jVSGNLAi0sCKIJMj1XhZ33vf/i7Ffy3+wt0bcG/SDvfGbZEFhKDuORyZlx+FB7cCoO46BM/BT9vyFx3fh7kwH+2gMcgCKqCoUhN1j6HWZ0TcMpTJo0k7dGeOYCnMd2nMNyvYuQ6Ppu1xb9IOWUAFNL0fRertR+F+60oX6u6P4PnIs7b8umN7MRfk4TnkGWSBFVBUiUDTazhFh3bY4tefOYpHVGeU9aOQ+vijqF4XddfB6M5El2svhEytQtugFtoG1oswVdVKaBvUwr1ySLnFdKvMNcvx6tgDbYduuFWoTMCoZ1H4B5K1aQ0A/sOfJPiF4j/y+Uf2o2nWGq8uvVEEBuNesw6Bo1+g8MJZTJn3pxfihsd6dmDl1n2s2raPywlJfDNvOUnpmQzubB0f/eOfq/ng5/m2/Ov2HGbi9Pm89lh/IqtXIS0rh7SsHPIKinu1BndpQ3ZePl/PW05sYgq7ok/x+6qNPNK17X3Zh437C+nTVkXjWm5UDJAxZoAWvcHC/hPF89OeHuDJ4M7FF79bDxWi8ZAwoqeGIF8Z9Wu40buNmi0Hi/djZC8NLeu7M2N5DkU6C1q1FK1aisLJdpBEIqFrn0f5e+lvHN63hfjYC/z6w0TclO5EtSse5jXr+w9YOu8H2/uufUZw6ug+1i6fTWL8ZdYun82Z4/vp2rd4bkPDZm35e+lvHDu0k7SUaxzet4X1fy2gcYuOAGi03lQKq273ksnkePn4E1KxinM7dH2f6rZ6nGPbZnLl1EYyks6zY+k7yBXuVGtYvBrZ9iX/4+D6bwGQK5T4Bte0eyk9PFEo1fgG10Qmt67pb9Dlk37tDOnXrCtM5WXEk37tzG3nWtyNEb07s2rLbv7auofLCYlMmbOE5LRMBnWxHqc/LVzJhz/NtuVfv/sgk6bN5pVRg4msUZX0rGzSs7Ltjv3ZK9ex//gZEpJTuZKQxB9rNrF25z56tG1+69ffM4lEQuOOj7N//Qxijm4k9dp5/pk3AbmbO7WbFdf92jlvsWPVN7b3+9fP4MqZ3WSlXSU96SKHNv/O6f2rqNOsuDG5adEkzhz8i95PfoObUk1+dir52akY9He8JVomw/v24O/N2/l783auxCcw9bcFJKelM6CbdUXB6fMX8/H3M+zKxFyOJeZyLIVFRWTl5BJzOZbLVxNsn7du2pCV6zezadc+riWncvDoSX5ZuIw2TRshkz3cvRDCg+fUaX7BggXMmjWL3r17M2nSJEaMGEF4eDj169dn3759vPJK6XeL7pXh3BEkHmrcW/WwDqVJSyR/2c9Yrg+BkGq8kHreNETCoCd/yU+4dx6CZtSbWArzMZyLpmhXcbe0ROOF5+i3be+VzbugbN4FY1wM+Yumiviv0508gESlRtWxn/VhbckJZM+bgvn6REippxfSmydD6nVk//4Vmj4j8XnhA8yF+ehOHCB/03K77cr8glBUqUn+71+5LFaH8Z/YT55ag7pzf6Se3hiT48me/c1N8Xsjuyl+i15H1m9f4tl3FL4vTsJckIfuxAHyNiy15cnfugoLFtTdhiDT+lgfNncmmvyb8pQHryaRtNw8z/a+ztfvAHB17nKOj5lQXmHZydu3gxRPLX6DHkPm7YP+aiwJX7yPMS0FAJm3L3L/4uEHOTs2IvXwwLt7PwJGPoO5IJ+CU8dI++PX+x5rt5aNyc7L55cV60nLyia8Ugjfv/kcIQHWoTJpWTkkpRcPH1q+ZQ8mk5kvZi/li9nFx0Kfts358HnrnfJgPx9+fPsFvp23ghETviDAx4vhPdozuq9rHxR2wz+7C3CTSxjZyxO1h5RL8Qa+mZdl98wHXy8Z5pvavpk5Zr6Zn8Xw7p589IIHmTlmNu0vYO3u4vkQnZpZV5x6+wn7oWm/rsxh9zHnLqZ6DhyNXq9j/szPyc+zPmzujYk/2fVYZKQmIb2px696rQY8/8ZnLP9jGisW/kxgUCWef+Nz2zMiAB595i1W/PEz82d+Tk52Jt4+/nToNph+Q59xKs67Ub/d05gMOvb89RH6whwCKtWn+5O/2PVY5GUllrhLfCdpCadY+8to2/v9a78AoEbjAbQbMvme4+7aqinZefn8tmyN9UGLlUOY8vaLhARYz5XpmdkkpxU34ldu2onJZOar3/7kq9/+tKX3bteCD8Za4yzS6fjyt4WkpmehdFMQViGYSS8+SddWTbkfmnd9BqNBx6ZFkygqyCakSgOGvPSbXY9FTqZ93Rv0BWxaNIm8rCTkCnd8g6rR64mvqNWkly3PsZ3W5YMXfVd84w+gx8jJRLa892c2dW7TguzcPGYvXkV6ZhZVQyvx1btvEBxovUGYnplFcpr9EL4n33jf9u9zF6+wcedeggP8WTrD2jgd/Uh/JBIJs/5YSmpGJt5aT1o3bcSzjz341b/ui//w06PLg8TixGOl1Wo1Z86cITQ0lJCQENasWUPjxo25dOkSjRo1IjvbuZUDsr962aly/wZeb/7w0Mbv9eYPpL73ZHmH4bSAT34nZcLj5R2G0wInz2WNwvETSP/tehvOcX6Ea1cSepBqLlxH7qHSnz3wb+fZtAdPTUop7zCc8tvEQHafLn0o1b9d6zoavlzm+uV4H5S3BkvJinb8vIF/O+9GnZi1qbyjcN4zXSD11P7yDsNpAXXvz5PFXaFw64Jy+V6Pjv8PHtTnBKeabJUqVSIx0brUXvXq1dmwYQMABw8eLNNSrYIgCIIgCILgUhJp+bz+o5za84EDB7J582YAXn31Vd5//31q1KjB448/zlNPPeXSAAVBEARBEARB+Hdxak7E559/bvv3kCFDqFy5Mrt376Z69er061f2FSwEQRAEQRAEQXj43HUjwmAw8Oyzz/L+++9TrVo1AKKiooiK+veOkRMEQRAEQRD+fxPPiXiw7no4k0KhYMWKFfcjFkEQBEEQBEEQHgJOz4lYuXKli0MRBEEQBEEQBCeJidUPlFNzIqpXr87HH3/Mnj17aNKkCWq12u7z+/mcCEEQBEEQBEEQypdTjYhffvkFb29vDh8+zOHDh+0+k0gkohEhCIIgCIIgPFhiTsQD5VQj4vLly66OQxAEQRAEQRCEh4RTjYhx48Y5TJdIJLi7u1O9enX69++Pr6/vPQUnCIIgCIIgCMK/j1ONiOjoaI4cOYLJZCIiIgKLxUJMTAwymYxatWoxbdo03njjDXbt2kWdOnVcHbMgCIIgCIIg2JP+dyc5lwenart///506dKFa9eucfjwYY4cOUJCQgJdu3ZlxIgRJCQk0K5dO15//XVXxysIgiAIgiAIQjlzqifiq6++YuPGjWi1WluaVqvlww8/pFu3brz66qt88MEHdOvWzWWBCoIgCIIgCEJpxMPmHiyneiKys7NJSUkpkZ6amkpOTg4A3t7e6PX6e4tOEARBEARBEIR/HaeHMz311FOsWLGC+Ph4EhISWLFiBWPGjGHAgAEAHDhwgJo1a7oyVkEQBEEQBEEQ/gWcGs40Y8YMXn/9dYYPH47RaLRuSC5n9OjRTJkyBYBatWrxyy+/uC5SQRAEQRAEQSjNf/jp0eXBqUaERqNh1qxZTJkyhUuXLmGxWAgPD0ej0djyNGzY0FUxCoIgCIIgCILwL+JUI+IGjUZD/fr1XRWLIAiCIAiCIDjFInoiHiiJxWKxlHcQgiAIgiAIgnAv8vb9VS7fq2nRr1y+t7zdU0+Eqx3t1ra8Q3Baww07Kfj1g/IOwymqMR9xemDn8g7DaXVWbCbh1WHlHYbTKn6/iPMjepR3GE6puXAdaxQR5R2G03obzpH63pPlHYbTAj75nTZ9t5d3GE7Ztbo9Fy9dKu8wnBZerRrfrnp478GN6y8hc/LY8g7DKT4TpvHNyoe37t8YICHn29fKOwynacd9V94hlE4s8fpAiX4fQRAEQRAEQRDuimhECIIgCIIgCIJwV/5Vw5kEQRAEQRAEwRliYvWDJWpbEARBEARBEIS7InoiBEEQBEEQhIefmFj9QImeCEEQBEEQBEEQ7kqZGxFTp06lqKgIgLi4OMTjJQRBEARBEAThv6nMjYhx48aRk5MDQNWqVUlNTb1vQQmCIAiCIAjCXZFIy+flhGnTplG1alXc3d1p0qQJO3fuLDXv8uXL6dq1KwEBAWi1Wlq2bMn69evt8syePRuJRFLidaMD4H4o855XqFCBZcuWERsbi8ViIT4+nri4OIcvQRAEQRAEQRBKWrRoEa+99hrvvvsu0dHRtG3blp49e5Z6Db1jxw66du3K2rVrOXz4MB07dqRv375ER0fb5dNqtSQmJtq93N3d79t+lHli9XvvvcfLL7/MSy+9hEQioVmzZiXyWCwWJBIJJpPJpUEKgiAIgiAIwu1YHpKJ1d9++y1jxozh6aefBuC7775j/fr1/Pzzz0yePLlE/u+++87u/WeffcaqVatYvXo1jRo1sqVLJBKCg4Pva+w3K3Mj4tlnn2XEiBHExsZSv359Nm3ahJ+f3/2MTRAEQRAEQRD+1XQ6HTqdzi5NqVSiVCpL5NXr9Rw+fJi3337bLr1bt27s2bOnTN9nNpvJzc3F19fXLj0vL4+wsDBMJhMNGzbk448/tmtkuNpdDeTy9PQkMjKS33//ndatW9OgQQOHrxsWLlxIfn6+y4MWBEEQBEEQBDvlNCdi8uTJeHl52b0c9SgApKWlYTKZCAoKsksPCgoiKSmpTLv5zTffkJ+fz9ChQ21ptWrVYvbs2fz1118sXLgQd3d3WrduTUxMjPP1eQdOPSdi9OjRZcr33HPPERUVRbVq1Zz5GkEQBEEQBEH4V5swYQLjxo2zS3PUC3EzyS1Dr25MCbiThQsX8uGHH7Jq1SoCAwNt6S1atKBFixa2961bt6Zx48b88MMPTJ06tSy7cdfu68PmxDKwgiAIgiAIwv9npQ1dcsTf3x+ZTFai1yElJaVE78StFi1axJgxY1iyZAldunS5bV6pVEqzZs3ua0+EeNicIAiCIAiC8NCzICmX191wc3OjSZMmbNy40S5948aNtGrVqtRyCxcu5IknnuCPP/6gd+/ed64Li4WjR48SEhJyV/HdjfvaEyEIgiAIgiAIQrFx48YxatQomjZtSsuWLZk5cyZxcXE8//zzgHV4VEJCAnPnzgWsDYjHH3+c77//nhYtWth6MTw8PPDy8gJg0qRJtGjRgho1apCTk8PUqVM5evQoP/30033bj4eyEeHXdwCBj4xA4etHUewVEn6eSv7J46Xm9+nUlcBHHkVZsRKm/HxyDu3n2syfMOXm2PLI1BqCn3wG79btkXlq0CclkjDjJ3IP7nN5/IujY5hz4BxpeYWE+3sxvlMjGlcOcJj3UFwKz/y5tUT68jE9qeqnBWDz+Xh+3Xuaq1l5GM1mQn08GdUsgj51q7g8dp8e/fAbMBS5jx+6q1dI/nUaBWdOlJpf264z/gOG4VahIqb8fPKjD5I8Z4at7r279sK7QzeUodZYCy+eJ2XBrxTFnHN57ADqNt3QdOqLTOuNISme7OVz0F866zCv96MvoI7qUCLdkHiVlM/HAyAProS211AUlaoi9wska/kc8revvS+xO+LVtQ++fYYg8/ZFHx9L6tzpFJ47VWp+z9Yd8e37CIrgCpgLCsg/dojUBbMw5+U+sJjvxLdNU6q9MQavxpG4Vwjk0OCxJP+1ubzDwr15R1RteyLVeGNMSSB/7R8YYm/TTSyTo+rYD/eGLZFqvDDnZFKw7W+KjhQ/UEji7oG6y2Dc6jZB6q7GlJlK/rpF6M+Xfj67F0+NCKNf9xA8NXJOn8/l2+kxXI4ruG2ZR/pVZGDPCgQFKMnKMbBtTxoz5lxCb7DYtvnUo1XsyqRn6un/+F6n4/z7779ZtnQpGRkZhIWF8exzzxEZGVlq/hPHjzNr1ixiY2Px8/Nj8JAhJe7U5eXlMWfOHPbs3k1eXh7BwcE8/fTTNGveHICCggLmzZ3Lnr17yc7KIjw8nOeee46aERFO78fNLBYLhzf+yJn9i9EV5hAYWp82Az7AN7hGqWUundhA9JYZ5KTHYTYZ8fIPo367J6nZpL8tj9lk5NDGH7kQvZqC3DRU2gAimgykcecXkEhdM9hA2bgdyqguSDVemFITKdy0BGP8xdILyOS4t+6FW2QzpGot5twsivasQ3/ceky4NWiNW70oZP4VADAlxVG4fRWmxFiXxHsri8XC4U0/cvamum/d//Z1f/lkybqv1+5JajYurnu9Lo9D66dy5dQmCvPS8a9Qm5b93iWwcj2Xxa5o0Bpl005I1FrM6UkUbVuBKeGSw7yyStVRD32pRHre759hzkyxvpFKcWveFbc6zZBovDBnplC0czWmK47/Dj5sLE4++O1BGzZsGOnp6Xz00UckJiYSGRnJ2rVrCQsLAyAxMdHumREzZszAaDTy4osv8uKLL9rSR48ezezZswHIysri2WefJSkpCS8vLxo1asSOHTtofv0cdz88dI0I7/adqPj8K8T/8C35p07g37sf1T79irNPj8KQmlIiv7puPULffJeEGT+Qs28PCj9/Kr06nsrj/seVSe8CIJHLCf/8WwxZWVz5+H30aSm4BQRiKrz9H1dnrD8Tx1ebjzKha2MaVgpg2dELvLR0B8vG9CBEqy613Mqne6F2K/7v8lEVj73zcnfj6ZZ1qOKnRSGVsvPiNT5cewBflZJWVV3XjaVt3YHgp8aSOHMqBWdP4tOtD6HvT+bCK09hTCtZ9x61I6n4yv9I/v1ncg/uRe7nT8jzrxEy9g3iv5gIgLpuA7J3bqHg7CksBj1+A4cRNvFLLr4yBmNGmstiB/Bo1BKvgaPJWvIr+svnULfqgt/zE0iZPA5TZnqJ/NnLZ5Oz+o/iBKmMoP99SeHR4oalxE2JMS2Zwuh9eA183KXx3ommRTsCH3+O5N9+oujcKby69KLi259wZfyzGNNLPlHePaIuwWPHkzp3JnlH9iH39SdozMsEP/sa1779+IHGfjsytYqc4+eIn7OcJkt+LO9wAFBGNkfT61HyVs/DEBeDe7MOeD0+joyp72LOznBYRjt8LFKNltwVv2NKT0aq1oJUVpxBJsPriTcx5+eQs/AnzDmZSL18sejuz9NFHxtcmWEDKvHpd+e4mlDA6GFhTPmoPiNeOEhhoeNn+3RtH8jzo6vx+dRznDiTTeWKKt591XpR/cMvxReRl2Lzee29Y7b3ZrPzcW7fvp2ZM2Yw9sUXqVOnDv+sXcsH77/P9Bkz7CYR3pCUlMQHH3xAjx49GP/mm5w+fZppP/2El5cXbdq0AcBgMPDuO+/g7e3NO+++i7+/P2mpqXioVLbtfP/998ReucL48ePx8/Njy5YtvPPOO0yfMQN/f3/nd+i6Y9t+4fjO2XQYOhnvgCoc2TydNbOeYtib/+DmrnFYxl3lRePOz+MdUA2pXEHcmW1sW/IOHhpfKke0BeDotl84s+9POgz7HN+g6qTGn2Tb4ndw8/CkXpt7PycpajfBo8sQCtb/iTH+EspGbdAMe5HsWR9jycl0WEY9YAxStZaCtfMxZ6YiUXnaHfvysBoYTh+iMP4SFqMB9xZd0Qx/mZxZH2PJy77nmG91bPsvnLhe917+1rpf+8tTDH3zH9yUjute6eFFo07WupfJFcSe2cb2Je/goS6u+x1L3yczKYaOw75ApQ0kJvov1sx6kqFvrEHtdfux7WUhr9kI9w4DKdq8FNO1yyjqt0I18Dny5kzGkptVarm83z7Foi8+j1gK84r3q3VvFLWbULRxEaaMFORhtVD1e4r8hd9jTk2455iFshs7dixjx451+NmNhsEN27Ztu+P2pkyZwpQpU1wQWdnd1yZbWFgYCoXCpdsMGDyMjHVryFj3N7qrsSRM/wFDagr+fQc6zK+qXRd9chJpK5ehT0ok/9QJ0tf8hapm8d0l3+69kXlqufzhBPJPn8CQkkz+qRMUXbrNnRYnzT90jgH1qzKoQTjV/LS82bkxwZ4eLIm+/Xf5qpT4azxsL9lNd5iahgbSqWYlqvlpqeyj4dGmNakR4EV0vGsvwv36DSFz8z9kbVqLPj6O5N+mYUhPwbdHX4f5VTVrY0hNJmPNCgwpSRSeOUnm+r/xqF7Tlifhu8lkrvsL3ZWL6BOukjjtW5BIUNd3/brGmg69yd+3hYJ9WzAmJ5C9Yg6mzHTUrbs5zG8pKsScm217uYVWQ+KhpmD/NlseQ9xFcv5aQGH0HixGg8tjvh2f3oPI3rqenK3r0F+7SurcGRjSU/Hu2sdhfo/qtTCkJpO1fhXG1GSKzp0ie/NalNVqOsxfXlLX7+D8xO9IWrnxzpkfEI/W3Sg6vIOiwzswpSaSv3YhpuwMPJp3cphfUSMSRZUIsudOwXDxNOasdIwJlzFevWDL4964LVKVmpwFP2CMu2DNExuDKenqfdmHR/pVZO7iOHbsTeNyXAGfTjmLUimjW/uSF+Y3RNbScuJMNhu3p5CUouNgdCabdqRQq7qnXT6TyUJGlsH2yspx/rewYsUKunXrRo8ePQgNDeW5558nICCANWvWOMy/ds0aAgMDee755wkNDaVHjx507daN5cuW2fJs2LCB3Nxc3v/gA+rWrUtQUBB1IyNtKwfqdDp279rFU2PGUK9ePSpUqMDIkSMJDg4u9XvvhsVi4cSuuTTu9DzV6nXDN7gmHYd9jtFQxIWjf5darkJ4FFUju+ITFI6XXyj12jyOX3AESVeO2PIkx0YTVrczYbU74OlbiWr1e1CpZmtS40/ec9wA7s07oT+2B/2xPZjTkyjctBRzThbKRu0c5pdXq4M8tAZ5i3/CeOUc5uwMTImxdnfPC/6aje7IDkwp8Zgzkin4ZwESiQRFlVouiflmN+q+UafnqRp5S91Hl63utdfr3vemujcairh8cgNRvcYTUq0ZXv5hNO36MlrfSpzet9AlsSubdMBwcj+Gk/swZySj27YCc24Wbg3a3LacuTAPS0Gu7cVNi9woajdFt38TxstnsGSnYzi+G+OVc7g17eiSmIX/lvvaiDh58iSVK1d22fYkcjmqGjXJPXLALj338EHUdRx3deefPonCPwDPZtZlr+TePni37UDO/uKudq+Wrck/c4pKL4+j7qJVRMycQ+DwUeCiruAbDCYTZ5IyaVnF/mmCLaoGcyzh9hf8w+dsoOtPq3juz60cjE0uNZ/FYmF/bDJXMnNpUsoQKafI5biH1yT/6CG75Lyjh/GoVddhkYKzp5D7+aNpbO1Kk3n5oG3VjtzD+0v9GqmbEolMjsnVw2tkMhSVq6E7Zz9MRHfuGG5Vy3YRrWrRCd35E5gyXds4c4pMjnvVGhQcP2KXXHD8CO41azssUnj+NHJff9QNrU+bl3l5o4lqQ370AYf5hetkMuQVqqC/YD9MTH/hFPLQcIdFlLUaYbx2GVXbnvi+9S0+r01G3WMYyItvqrjVaoQh7iKaviPxe/s7fF7+GFX73nAfnrhaIcgdf18lB6KL7xwbjBaOnswispa21HLHT2cTEe5J7Rqetu20aOrL3kP2PXeVKniwcnYLFv/SnA/frE2FIHen4jQYDFyIiaFx48Z26Y0aN+bM6dMOy5w5e5ZGt+Rv0rgxMTExGI1GAPbv20ft2rWZ9tNPPDpiBC88/zyL/vwTk8naA2MymTCbzbjdctPLzc2N06dKHx5YVrkZ8RTkplKpZmtbmkzuRki1ZiTHRpdpGxaLhfiYvWSlXiakalNbenDVJiRcsKYDpF87S9KVI4RGOL7IvytSGbLgUAyXz9glGy6fQV7J8dLtihr1MSXG4d6iK14vfYb2uYl4dBpkd+yXLOQGUhmWItc/Vyo3I57C3FQq1bi3uk+4sJfsm+rebDZiMZuQKexX5JEplCRdOXzvgUtlSIMqYYy1H2ZkjD2LrEKV2xbVjByP5tlJqIaMRVa5uv2HMjmYbmnkGw3IK/w/WYq/nJ4T8V/l1HAmk8nElClTWLx4MXFxcej1ervPMzIcd+/fK5nWC4lMjiHTvgvVkJmJp4+vwzIFp08S+8XHVHl3ElI3NyRyOdl7dhL/03e2PG4hFdA0DCZzy0YuvfcmyoqVqfTS60hkMpIXzHZZ/JkFekwWC75q+z+wfip30vMdD2HwV7vzfvem1A7yQW8ys+bUFZ5btI1ZIzrSpHLxHcRcnZ7u01ZjMJmQSiRM6NqEFlVc9+hzuacXEpkMY5Z93ZuyMpF7O677wnOnSZgymYrj30eqsNZ97oHdJM36odTvCXz8GYwZaeQfc8FJ+CZStRaJTIY5x76r3JSbjdLT+87ltd64125I5tz7s9by3ZJprftjzL7l/yM7E7mX4/+PopgzJP34JSGvTEBy/f8j79BeUmZPexAhP7SkKk/rsZOXY5duyc9GqnF880LmE4AitCYWg4GcBT8gVXui6TsKiYeavBW/WfP4BiDzrk3R8b1kz52CzC8ITd9RIJVRsPUvl+6Dr48bABlZ9ufqzCw9QYGlX/Bv3pmKt5eCaV80RCIBuVzKirUJzF9a3Fty+nwun0w5y9WEQny9FYweFsbPXzVi1IsHyck13lWcOTk5mM1mvH187NJ9vL3JzHQ8dCYzMxMfb2+7NG8fH0wmEzk5Ofj6+pKUlMSxY8fo2LEjkz76iGsJCUybNg2TycSjjz2GSqWidu3aLFy4kMqhoXh7e7N9+3bOnTtHhQoV7mofHCnItQ4v9ND42aV7aPzIy7p227K6wlzmf9oes1GPRCqlzcCJdo2Rhh2eQV+Uy6KveyGVyDBbTDTv/hrVGznukbwbEpUGiVSGOd/+po4lP8c6PM8Bmbcf8srhWEwG8pbNQKLSoOo2HIm7ioK18x2W8egwAHNeFobLrh+Xb6t7Twd1n3n7utcX5jL/s/aYjHqkUimtBxTXvZtSQ1BoQ45snoZ3YDU8NP5cPLqGlKvH8fILu+e4JR5qJFIZllvrviAXicpx3Vvycyjc+Cem5HgkMjmK2k1RDRlLweIfbT1BptizuDXugCn+IuasdGShNZCHR/6nL4QF5znViJg0aRK//PIL48aN4/333+fdd9/lypUrrFy5kg8++OCO5Ut7PHiZ3fL8CeuNO8fPpFCGVqHS2FdJWjCb3EMHUPj6UeGZsVR+dTxXv/3i+gakGLOyuPrdV2A2UxhzHoWfP4FDRri0EWGL95b3Fts+lFTFT0sVv+ITRoOK/iTnFjL3wDm7RoTaTcGfT3SjUG9kf2wy32w9SiVvDU1DSx+q4BISSvx/3OBWKYzgp18kbfE88qIPIffxJWj0c4Q8/zqJP31dIr/fgGF4tenIlfffwGK4X0ODbo1V4iCtJFXzDpgL8yk8cfC+ROUyEknp/x8VQwl84gXSl/9B/vHDyL19CXjsaYLGvELyzAc7jvLh5OjYKYXUelzlLpmJRVcIQN4/f6IdPpa81fPAaACJBHN+DnkrZ4PFgvFaLFJPbzza9rznRkTX9oG8+WJxD9tbH50oZRcktz38G0V68fjQML6ZHsPpc7lUCnHn1Werk5ahZ84i66S/fYeLbxpdioWTZ3NYNCuKnp2CWbQq3qn47/ohTA7y38xsseDt7c3Lr7yCTCajRo0apGdksGzpUh597DEAxo8fz5QpUxg1ciRSqZTq1avToUMHLly4wN2KObKaHcsn2t73fHK6wzivJ952W25KNUNeW4FBX0BCzF72rv4crW8lKoRHAXDx2Fpijqym84iv8QmqTvq1s+xZ/RkqbSARTR0P8717txwkktucNyVSsFjI/+t3uD6/p3DzMtSDnqZgwyLrsX8TZVRX3Oo0JW/Bd2C6u0anIzHRq9l5U933uF73klvr+XZ/eK9TKNUMftVa99cu7GXf3/Z133H4l2xf8g4LPm2PRCrDv0IdqjfsQ1qC414z1yi97s2ZKcUTqAFT4hUknt64Ne1E4fVGRNHW5bh3HY76iXcAC+asdAyn9qOoG3UfY35wLPehJ1conVONiAULFjBr1ix69+7NpEmTGDFiBOHh4dSvX599+/bxyiuv3Lb85MmTmTRpkl3axIkTGXCH7zXlZGMxGVH42t9plXv7YCzlLlXQ8JHknzpB6hLrGMWiyxeJLyqkxpRpJM7+BWNGOsaMdCxGo91swKK4Kyj8/JDI5dbPXMBH5YZMIinR65BRUISvquzd//Uq+LH21BW7NKlEQqiPdchBRJAPl9Nz+G3fGZc1Ioy52VhMJuTe9ncIZV4+Je6G3+A/eASFZ0+RvnIxALrYSyTqiqj62fek/vEbxsziiw+//o/gP+RRYie+iS7W8coT98Kcn4PFZEKq9baP31OLOffOE/nULTpQeGgnmBxPQH3QTDnW/ZF73fL/ofXGWMpkR9/+wyg8d5rMv5cCoI+7TLKuiNAPvyFt8RxMWfenB/FhZy7ItR47Gi+7dIlai7mUSaDm3GzMOZm2BgSAKfUaEqkUmZcvpvRkzLlZYDbZNfpMqYnIPL1BJrunY23XgXROny8eeuimsN5l9PVxIz2zuDfCx0tRonfiZk+PrMr6rcn8vcG6nOCl2Hzc3WW89VJN5i6Oc9heLdKZuXQln0oVPO46bq1Wi1QqJfOW3uys7Gy8b+ltsO2Dj0+JXorsrCxkMhlarfUGjK+PD3K5HJmseHJv5cqVyczMxGAwoFAoCKlQgS+/+oqioiIKCgrw9fVl8uTJBAfffY9uWJ2ODAmtb3tvMlrruDA3DbW2+JxcmJeO6pY75LeSSKV4+VvvbPtXqE1WyiWit860XcjuW/MVDTs+Q/WG1tWo/EIiyMu6xtGtM++5EWEpyMNiNiFVa7n5aJSoPEv0TtxgzsvGnJdla0AAmNKTkEikSD29MWcWL/qgbN4F91bdyVs4FZOLJvWG1elIYOWSdW9dueqmus9PL9EzdKtb6z4z5RJHb6p7rV8ofZ+fj0FfgKEoD5U2kE0LXsfTt9I974elMB+L2YREbT//SKLSWOc5lJEpMRZF7eLhb5bCfAr/+hVkciQeaix52Sjb9sWcXXJxEUG4E6f6r5KSkqhXz7qEmUajITvb+oe0T58+ZZqENmHCBLKzs+1eEyZMuGM5i9FIQcx5PBs3s0v3bNyM/NOOJ5FJlUostywVcuv7/FMnUFaoaHdXQlmxMob0NJc1IAAUMhm1g33Yd8X+KYX7riTToGLZV/84m5yJv+b2f6AtgN6VF7xGI0UXz6Nu0MQuWdOgCYVnHY8ZdlT3xQ214rr2GzAU/0dGEvfR2xRdPO+6mG9mMmG4egllRH27ZGVEffSXb/+dbtXrIA8IIX9fyaV2y43JSNHlGFS3TEBX1WtE0fkzDotI3JRgKeX/Q9y8KZ3JhPHaFdyq28/9cateB2Oc4wURDLExSD29wa24h1XmF4zFbMZ0fTUnY9wFZL5BducdmX8wppzMe26sFhaaSEgssr0uxxWQlqGjWcPiRqdcLqFhpDcnz+aUuh13pRSL+Za7+mbro5VKu+GnkEsIq6yya6yUlUKhoHqNGkRH249Vjz5yhNp16jgsU7tWLaKP2M8NOnLkCDVq1EAut94nq1O3LteuXcN80/koISEBX1/fEot/uLu74+vrS25uLkcOH6ZFixZ3vR9u7hq8/MNsL5+g6qg8A4iP2WPLYzLqSbx0kKCwu1tEwoLFdmEMYDQUIrllKIpEIsVy62/dGWYTpqQ45FXt51kpqtbCGO/4Zo8x/iJSjTfcNFdA5huIxWy2NpyvU0Z1waN1T/IW/YgpKa7khpzkpixZ9x4uqnssFkymkse1wk2FShuIriCb+PO7qFLH8YILd8VswpwcjzzUfolheVgEpmtXyrwZWWBFLPkObnaYjNaVsKRSFDXqY7zomon45c0ikZbL67/KqT2vVKkSiYmJAFSvXp0NGzYAcPDgwTINS1IqlWi1WrtXWYczpS5bhG+PPvh274WychgVnn8ZRWAgaX+vBCDkqecIffNdW/6cfXvwbtMevz4DcAsOQV2nHpXGvkr+2dMYM6wt77S/VyLTelHxhVdRVqyMtnlLgkaMIu2v5XdTLWUysmkEK45fZuXxS1xKz+HrzdEk5RQwpKF1gubU7cd5b03xEqILDp1ja0w8sRm5XEzLZur242w+H8+wxsXrW/+67zT7riQRn5XH5fQc5h08x5pTV+hV597HZd4s/a+l+HTphXfnHrhVCiXoyRdQ+AeSuX41AIEjx1Dhlf/Z8uce3Ie2RVt8uvdFERSCR626BI95kcLzZzBeX1LVb8AwAh59kms/fo0+JQmZtw8ybx8k7s5NzLydvG1rULfohCqqA/KgingNfByZjz/5u62rAGn7jMDnsRdLlFO36Ij+SgzGRAer5shkKCqGoagYhkQuR+blg6JiGDL/e1/e704y1yzHq2MPtB264VahMgGjnkXhH0jWJmtD3n/4kwS/MN6WP//IfjTNWuPVpTeKwGDca9YhcPQLFF44iynz39MLIVOr0DaohbaBdaUWVdVKaBvUwr3y/Xvq5p0U7t6Ae5N2uDduiywgBHXP4ci8/Cg8aG1YqrsOwXPw07b8Rcf3YS7MRztoDLKACiiq1ETdY6j1GRHXh3MUHtiKRKVG0+tRZH5BuNWsj6p9b4r2b7kv+7DkrwRGPRJKuxZ+VA1V8e5rEeh0JjZsLx7+8N7rETz3eFXb+90H0hnQqwKd2wYQEuRO04Y+PP1YVXYdSLe1P198qhoNI70ICXKnTk1PPplQF7VKxj+bk24NoUwGDhzI+vXr2bB+PXFxccycMYPU1FR69eoFwO+//87XXxcPh+zVuzcpKSm2hzVtWL+eDRs2MGjwYFue3r17k5uby4zp04mPj+fAgQMsXrSIPn2K5w0cPnyYQ4cOkZSUxJEjR5jw9ttUrFSJrt0cr952NyQSCfXaPE70lhlcPrmRjKTzbFs8AbnCneoNi2PY8uf/2P/PN7b30VtmEH9+NznpV8lMucTxHb8Tc3gVNRr1s+UJq92R6C3TiT2zjdyMeC6f3MjxnbOpEtn1nuMGKDqwBWWDVrjVb4nULxiPzoORan3QR1ufd+Levj+qPqNt+fWnDmEpzEfdexRSv2Dklavj0Wkg+uN7bMe+MqorHu36kr92HubsDCRqLRK11q7h4So36v7o1pvqfsn1ur9p3sjWRf/jwM11v7W47rOu1/35I/Z1f/XcTq6e20lORjzx53fz98zReAVUJaLpIJfErju8DUW9FijqRiH1DULZfgBSTx/0x3YDoGzTB/cej9nyuzVqjzy8HlJvf6R+wSjb9EFRsyH6o7tseWTBYcir10fi5YesYjVUg54HJOgO3Z/zjvD/m1PDmQYOHMjmzZuJiori1VdfZcSIEfz666/ExcXx+uuvuzpGO1nbtyDTagl+7Ankvn4UxV7m0ntvYUixrlik8PXDLbD4Ai5j4z9IVSr8+w2i4rMvYsrPI/foERJ/+dmWx5CawsUJ46j4/MtEzPgdQ1oaqSuWkrJ4gcvj7147lOwiHTP3nCItv4jq/l78MKQtFbysz4hIyy8kKaf4+RQGk5kpW4+RkleIUi4j3F/L1MFtaRtePNmvyGDisw2HbXmq+HrySe8WdK8d6tLYc3ZvQ+apxX/oKOQ+vujirhD3yQTb8znkPn4oAoq7i7O3rkfq4YFPrwEEPfk8pvw88k8cJWXuLFsen579kCrcqPy/D+2+K/XPOaQumuvS+Auj9yJVe+LZfTAyLx8MiVdJn/G5bbUlqdYbmY9997bE3QP3BlFkL5/tcJsyL18C3/rS9t6zcz88O/dDF3OKtB8/cmn8t8rbt4MUTy1+gx5D5u2D/mosCV+8b3tmh8zbF7l/8f9Hzo6NSD088O7ej4CRz2AuyKfg1DHS/vj1vsZ5t7yaRNJy8zzb+zpfvwPA1bnLOT7mzj2W94Pu5AEkKjWqjv2QenpZlwieNwVzlrUxLPX0Qup907Gj15H9+1do+ozE54UPMBfmoztxgPxNxTcmzNkZZM/+Bk2vEfi89DHm3EwK926kYMf9eVjhgmVXUbpJGfdCDTw1Ck6fz+H1D47bPSMiKMCdmzse5iyKxWKBZ0ZWJcDPjawcA7sPpDNz3mVbngA/JR+Or42XVkFWjoFT53J4bnw0yan2897Kqn379uTm5vLHH3+QkZFBlSpVmPTRRwQFWc/rmRkZpKYUN3yCg4P56KOPmDlzJn+vXo2fnx/PPf+87RkRAAEBAXzy6afMnDGDF8eOxc/Pj/79+zPkkUdsefLz85n9+++kpaXh6elJ6zZtGD16tK0341416PA0RkMRu1Z8hK4wm8DK9en9zK92z4jIy7pmN/fDoC9k54qPyM9OQq5wxzuwKh2Hf0n1hr1seVr3f4+DG6aya8VHFOalo9YGUjtqGE26OF5//m4Zzhym0EONe+teSDVaTKmJ5C2ehjnHeuNBqtEi1d40rNKgI3fhVFTdhqJ98m0shfnozxymcMdqWxZl43ZI5Ao0g561+67CnWso2nXvS+reqkH763W/8iP01+u+19O/2j0j4ta6N+oL2bXyproPqEqn4V8S3qC47vVFeRxY9y352UkoVd5UjexK8+6vI5W5Zml74/loijxUKFt0v/6wuUQKVszAkmsdvidRa5F63lT3Mhnu7fsh0XiB0YApLYmCFTMw3ry6llyOsnUvpF5+WAw6jJfPUPjPfLhp6KUglJXEcusMNCfs37+f3bt3U716dfr163fnAqU42q3tvYZSbhpu2EnBr3eeVP5vpBrzEacHdi7vMJxWZ8VmEl4dVt5hOK3i94s4P6JHeYfhlJoL17FG4Zon+paH3oZzpL73ZHmH4bSAT36nTd/t5R2GU3atbs/FS66f//SghFerxrer7vnPZ7kZ119C5mTXNDQeNJ8J0/hm5cNb928MkJDz7WvlHYbTtOO+K+8QSpVxYtedM90HvvVu/+yO/6/u+haLwWDg2Wef5f3337c9qCcqKoqoqP8fM/sFQRAEQRAEQbi9u54ToVAoWLFixf2IRRAEQRAEQRCcIiZWP1hO7fnAgQNZuXKli0MRBEEQBEEQBOFh4NSMserVq/Pxxx+zZ88emjRpglqttvv8Ts+JEARBEARBEATh4eVUI+KXX37B29ubw4cPc/jwYbvPJBKJaEQIgiAIgiAID5RFPPTogXKqEXH58uU7ZxIEQRAEQRAE4f8lpxoR48aNc5gukUhwd3enevXq9O/fH19f33sKThAEQRAEQRDK4r88ybk8ONWIiI6O5siRI5hMJiIiIrBYLMTExCCTyahVqxbTpk3jjTfeYNeuXdSpU8fVMQuCIAiCIAiCUI6carL179+fLl26cO3aNQ4fPsyRI0dISEiga9eujBgxgoSEBNq1a3ffn14tCIIgCIIgCABIJOXz+o9yqhHx1Vdf8fHHH6PVam1pWq2WDz/8kC+//BKVSsUHH3xQYtK1IAiCIAiCIAgPP6caEdnZ2aSkpJRIT01NJScnBwBvb2/0ev29RScIgiAIgiAIwr+OU3Mi+vfvz1NPPcU333xDs2bNkEgkHDhwgPHjxzNgwAAADhw4QM2aNV0ZqyAIgiAIgiA4ZHHu3rjgJKcaETNmzOD1119n+PDhGI1G64bkckaPHs2UKVMAqFWrFr/88ovrIhUEQRAEQRAE4V/BqUaERqNh1qxZTJkyhUuXLmGxWAgPD0ej0djyNGzY0FUxCoIgCIIgCMJtWf7Dk5zLg1ONiBs0Gg3169d3VSyCIAiCIAiCIDwExOAxQRAEQRAEQRDuyj31RAiCIAiCIAjCv4F4YvWDJWpbEARBEARBEIS7IrFYLJbyDkIQBEEQBEEQ7kXi2aPl8r0htRqWy/eWt3/VcKYrT/cv7xCcVuWXVeQeWFPeYTjFs3lvMiePLe8wnOYzYRpFC78o7zCc5j7if+QeWlfeYTjFs2kPUt97srzDcFrAJ7+zRhFR3mE4rbfhHANfiinvMJyy4scaZJzYVd5hOM23XhtWHzaWdxhO69tETsq7T5R3GE4J/HQ283c+vPc/R7aVkPHJc+UdhtN835tR3iEI/xL/qkaEIAiCIAiCIDhDzIl4sERtC4IgCIIgCIJwV0QjQhAEQRAEQRCEuyKGMwmCIAiCIAgPPfHE6gdL9EQIgiAIgiAIgnBXRE+EIAiCIAiC8NCzIHoiHiTREyEIgiAIgiAIwl0RjQhBEARBEARBEO6KGM4kCIIgCIIgPPTEcyIeLFHbgiAIgiAIgiDcFdETIQiCIAiCIDz0xMTqB+ueeiL0ej3nzp3DaDS6Kh5BEARBEARBEP7lnGpEFBQUMGbMGFQqFXXr1iUuLg6AV155hc8//9ylAQqCIAiCIAiC8O/iVCNiwoQJHDt2jG3btuHu7m5L79KlC4sWLXJZcIIgCIIgCIJQFhaJtFxe/1VOzYlYuXIlixYtokWLFkhuesR4nTp1uHjxosuCEwRBEARBEATh38epRkRqaiqBgYEl0vPz8+0aFYIgCIIgCILwIIiJ1Q+WU42IZs2asWbNGl5++WUAW8Nh1qxZtGzZ0nXRlcKzQ0+03Qci9/ZBfy2OjD9/RRdz2mFe/ydfQdO6c4l0fUIc1ya+bHsv9VDjPXAkqsYtkKk1GNKSyVz8O4UnDrs8/iWbdjNvzVbSsnOoVjGYN0YOoFFENYd5txw8ztLNezgfl4DBYKRapWCeHdidlvVr2fI8++lPHDlbsgeodYPafD/+GZfGrmzcDmVUF6QaL0ypiRRuWoIx/ja9TzI57q174RbZDKlaizk3i6I969Af3wuAW4PWuNWLQuZfAQBTUhyF21dhSox1adw3LDpwhtl7TpCWW0h4oDdv9YiicVjwHctFxyUz5ve1VA/0YfELA2zpY35fy6HYpBL529aoxI+PdXNl6AAs2biTeWu2kJZ1/dgZNYhGtcId5t1y8BhLN+3ifOyNYyeEZwf3oGX92nb5cvMLmLZ4DVsOHSc3v4AKAX689lh/2jSs6/L43Zt3RNW2J1KNN8aUBPLX/oEhNqb0AjI5qo79cG/YEqnGC3NOJgXb/qboyE5bFom7B+oug3Gr2wSpuxpTZir56xahP3/c5fGXhW+bplR7YwxejSNxrxDIocFjSf5rc7nEcqthvXzp1toLtYeUmNgiZi5K5WqSvtT8H79akcgaqhLph07m8+n0awDUCXdnQBcfwkPd8fWSM3nmNQ4cz79v+3DDsnVbWPDXetIzs6hauSKvPTGchnVqOsy7bd9hlm/YRsyVOPQGI9UqV2DM0P60aBh53+O8wWKxsGHZNPZvWUJBfg6h1esz6Mn3CK5UvdQySfEXWL/kB+IvnyYz7Rr9Rv2Pdj0ft8tTVJjP+iVTOXFoM3nZGVSsUpv+j79NaHg9l8XuEdUJVZueSD2tv9u8NX9giD1fegGZHHWn/rg3aInU0wtzdib521dTdNj6u/Ue8zZu1WqVKKY7d4zsuVNcFvcNFouFHX/9yJEdiykqyKFi1fr0eOwDAivWKLXMkR2LOb53FakJ1vNTSFhdOg58nYrV6tvyTP1fJ7LTr5Uo27Tjo/R87AOXxK5s0h73lt2u/829RsGGxRivXii9gEyOR9veuNWLsv3NLdy1Fv2xPSWyutVpimbQM+jPHSVvyc8uiVf4b3GqETF58mR69OjB6dOnMRqNfP/995w6dYq9e/eyfft2V8doR9WsDb7Dx5C+YAa6C2fwbNedoFc/IOGDlzBlpJXIn/7nL2Qum1ucIJNRYeJ3FBzefVOanKBxkzDlZpM6/QuMGenIff0xFxW6PP4N+6L5Zv5K3n5iMA1qVGX51j288tVMlnz+P4L9fUrkjz53kajImrw4tBeeKg9W7zjA69/+yuwPX6VWlUoAfPXqExiMJluZ7LwCHn33a7o0b+DS2BW1m+DRZQgF6//EGH8JZaM2aIa9SPasj7HkZDosox4wBqlaS8Ha+ZgzU5GoPEEqs30uD6uB4fQhCuMvYTEacG/RFc3wl8mZ9TGWvGyXxr/u5CW+XLefd3u3pGFoEEsPnWXs/A2seHEQId6aUsvlFul5b8UOmlerQEae/THx7bDOGEzFdZ9VqGPozyvpWqeqS2MH2LD3CN/MW8HbTz5Cg5pVWb5lD698OZ0lX04g2N+3RP7osxeJiqzFi0P74Kn2YPX2/bz+9SxmfzTOduwYjEZe/HwaPlpPvnjlSYJ8vUnOyER101wnV1FGNkfT61HyVs/DEBeDe7MOeD0+joyp72LOznBYRjt8LFKNltwVv2NKT0aq1todP8hkeD3xJub8HHIW/oQ5JxOply8WXZHL4y8rmVpFzvFzxM9ZTpMlP5ZbHLca2MWHfh29+WF+MtdSDAzp4cuHL1fkxY+uUKSzOCzzxaxE5LLiO3ueahlTJoSyJzrXluaulHIlQc+WfTn875kK930/ADbtPsB3s//kzadHUr9WdVZs3M64z77jjykfExzgVyJ/9JnzNK9fh+cfHYSnWsXfW3bx5udT+eWzd4moFvZAYt66+ld2/DOH4c99in9IFTavmMHMz57mrW/W4O6hdlhGryvEN7Ay9aO689f8LxzmWTLrA5KuxjDihc/x8gng8K6/mfnZ07z51V94+Qbdc9zKetbfbe7quRhiY/Bo1hGv0ePI+P6dUn+3XiPGIlV7kbPiN0zpKUg19uf97D9+QCIrvvyQqNT4vvQxuhMH7zleR/as+4V9G2fT78nJ+AVXYeff01nw7VOM/fQflO6Oz/2x5w4Q2bw3lcIbIVco2bPuFxZMGcPzH/2N1sdar2PeW4rFXHz+T0mIYcG3T1G7SXeXxO1WpymqbkMp+OcPDFcv4t64HZ4jXiZ7+oeYS/mbqxn0DFK1lvy/52LOSEWi9kQiLTlmX+rli6rLEAxxt7mJ8xD6L89PKA9O1XarVq3YvXs3BQUFhIeHs2HDBoKCgti7dy9NmjRxdYx2vLr2J3fXJvJ2bsSQGE/Gol8xZqbh2aGnw/yWwgJMOVm2lzKsOlKVhtxdxXcGPdt0QarWkPLTZ+gunMWUkYruwhkM8VdcHv+Cf7bTv30UAzq0oGrFIN4YOZAgP2+Wbt7tMP8bIwcyuk8n6lYLJTQ4gBeH9iY02J+d0adsebw0avy9tbbX/pPncHdTuLwR4d68E/pje9Af24M5PYnCTUsx52ShbNTOYX55tTrIQ2uQt/gnjFfOYc7OwJQYiynhki1PwV+z0R3ZgSklHnNGMgX/LEAikaCoUvIu1b2at/ckAxvXZFCTCKoFePNWzxYEe6lZfOjsbct9vHo3PetVo0GlgBKfeamU+HuqbK99F6/hrpDTtW4Vl8e/4J9t9O/QggEdW1L1ei9EkJ8PSzeVcuyMGsTovp2pGx5GaHAgLw7rS2hwADuPnLTlWbVtH9l5BXzz+tM0jKhGSIAvDSPCqRlW0eXxe7TuRtHhHRQd3oEpNZH8tQsxZWfg0byTw/yKGpEoqkSQPXcKhounMWelY0y4bHcXzr1xW6QqNTkLfsAYd8GaJzYGU9JVl8dfVqnrd3B+4nckrdxYbjE40qejN0vXZ7LvWD5xiXqmzktGqZDQrqlnqWXyCsxk5Zpsrwa1VOj0ZvZE59nyHDldwB9/p7Pv2P3vfbhh4eoN9O3Uln5d2lGlUgVef3IEgX6+LN+wzWH+158cwcgBPalTvSqVQ4J44bHBVA4OYtfhYw8kXovFws518+jc/1nqNe9KSOUaDH/hM/T6IqL3rCm1XGh4Pfo+Np5GrXohl7uV+NygL+LEgY30fvQNwms3xT84jO5DXsQ3sCJ7Nv3pkthVrbtTeHgHRYesv9u8tX9gzs7AI8rx79atRj0UVWqRNffb67/bNIzxlzHGFf9uLYX5mPOybS+36pFYDHqKTh5wScw3s1gsHNg0lza9n6d2k24EVqxJ/6c+x6Av4uT+v0stN/CZr2na8VGCQ2vjH1KNPqM/xmIxc/nMXlsetacvGq8A2yvm+DZ8AkIJi2juktjdo7qgO7ob3dHdmNOTKNi4GHNOJsom7R3mV1SrizysJrl//oDx8lnM2emYrl3BGH/JPqNEgnrAGAp2rMacmeqSWIX/JqcfNlevXj3mzJnjyljuTCbHLSyc7H+W2SUXnTqKe3jZLjo1bbtQdOYYpoziH45Hw2boLp3D79HnUDWKwpSbTf7+HWT/sxwsZpeFbzAaOXslnif62p98OFBi0AABAABJREFUW0RGcDzmSpm2YTabyS/SodWUHGJww6rt++nWohEe7sp7CdeeVIYsOJSivRvskg2XzyCv5HgolqJGfUyJcbi36IpbZBQWgw5DzAkKd6wGo8Hx9yjcQCrDUuTaCxKD0cSZa+k81aa+XXrL8Iocu5pSarmV0eeJz8zls0HtmbXj6B2/Z0X0eXpEVkXlprjXkO0YjEbOXr7KE33th+a1qBfB8ZjLZdqG9dgpsjt2dhw5Sf0aVfhi9hK2Hz6Bt1ZDj1ZNGN23CzIHd6+cJpMhr1CFgh1r7ZL1F04hD3U8HEtZqxHGa5dRte2JsmErLHod+rNHyd+03Hb8uNVqhCHuIpq+I1HWboQ5Pxfd8X3W77E4vrv+XxTkJ8fXS87RswW2NKPRwqkLhdSq5sGG3Tll2k6XVlp2HclDpy+/ujUYjJy7FMuogb3s0qMa1OHEudsM87iJ2WymoKgIrcZxD4CrZaTEk5uVRkT91rY0ucKN8NpNuXI+mpadhzq1XZPJhNlsQqGwP9crFO5cPhd9TzEDtt9t/g77ho7+wkkUoY6HYbnVbogx4TKqtr1wb3T9d3smmrybfre38mjSFt2J/WAofWids7LS4snLTqVaXfu6D4toRvyFaJq0H16m7Rj0hZhNRjzUXg4/Nxn1nNj3Fy26PuGauaFSGbKQUAr3rLOP49Jp5JUcnzMVNetjSozFvWV3lPWisBj06M8fo3D7X3Z179G2D5b8XPRHd6OoXPpwOkG4E6evEi5evMh7773Ho48+SkqK9SJs3bp1nDp16g4lnSfTaJHIZJhysuzSTTlZyLxKDgUqUd7LB4/IJuTutL9DqPAPRt2kFUilJH//Edl/L0HbrT9evR9xZfhk5eZjMpvx1drf+fP18iQtO7eUUvbm/7ONIp2ers0bOvz85MVYLsYn0b9D1L2Ga0ei0iCRyjDn28dpyc+xDjFxQObth7xyONKACuQtm0HBpqUoIhqh6jas1O/x6DAAc14Whsu37x24W5kFOkwWC35qD7t0P7UHaXkFDsvEpmfz/aZDfDaoPXLZnX8qJ+JTuZCSycDGES6J+Wa2Y8fLvq7v6thZu9V67EQ1sqUlpKSz+cAxTGYz37/1PGMGdGfB2q38tnLDbbZ096QqTyQyGeY8+4tVS342Uo3jP8oynwAUoTWRBVYkZ8EP5K9diLJuUzR9RxXn8Q1AWbcpSKVkz51CwbbVeLTugapDX5fG/7Dz1lrvF2Xl2j8YNCvXhLdW5qhICTXClIRVULJpj2uHGd6trNxch78FHy8vMrLKFtsfqzdQWKSjc6tm9yPEEnKzrUNtNV72Q600Wj9ys0oOwy0rdw81YTUasnHFdLIzUzCbTRzetZq4i8fJzbr3O8yl/W7NeTm3+d0GogiriTyoItkLppK35g+Ukc3w7DfKYX55parIgytTeOj+DIXOy7bWg0ZrX/dqrR95OWWv+y3LvsXTO4hqdVo5/Pxs9GaKCnJp0Hqg88He5MbfXEv+LXWfn4tU4/hvrtQnAHnl6sgCK5C7dDoFGxbjVrsx6h4jbHnklcJRNmxN/pp5Lonz38aCpFxe/1VONSK2b99OvXr12L9/P8uWLSMvz9q1ffz4cSZOnHjH8jqdjpycHLuXTqcrewC33mGUSMp011HTqhPmgnwKoveXKG/KySZ97jT0sRfJP7iT7DVL8OzQo+wx3YVb71JYLJYy3blYt/cIM5dv4LMXH8fXy/EQhFXb9xNeKZjI8Ps1ztdB3d+aZvtMChYL+X/9jikxFuPFUxRuXoZb/RYgL3mnXhnVFbc6TclfNgtM9+cp6CXqHgsSBycAk9nMhGXbeaFDY6r4O/5jeasV0eepHuhDPQfDnlzl1sPEYqFMp691ew4zc/k6Pnv5Cbtjx2Kx4KPV8O7Tw6ldtTLdWzbmyf7dSh1ed+9uPVZuE73UemzlLpmJMeEy+vPHyfvnT9wbtS4+fiQSzPk55K2cjfFaLLoTByjYthr35h3vU/wPh3ZNPfnjm3Dbyzav4dafL2XvsOnc0ovYazpiYu/iXH0flTxlWijLr2HDrv38ungVH497vkRDxFWO7Pqbd55sanuZrp/PSp5rynbuv50RYyeDxcLHL3bk7ccbsWvdfBq16u1wHLzTHP3NLYXk+t+EnMUzMMZf/92uXYh7ozYOz/seTdphTLqKMb5sPap3cmLfaj5/sbHtZbb9Lbn15Ono/8OxPf/8wsn9a3hk7A/IFY57+I/uWkr1yLZ4et/7PBQ7jn6fpf3JvX4tlL/yV0zXrmC4eJKCjUtxa9DSWvduStQDniJ/zTwshQ9u+KHw/5dTw5nefvttPvnkE8aNG4enZ/EFSceOHfn+++/vWH7y5MlMmjTJLm3ixIk8cYdyprwcLCZTiV4HmadXid4JRzRtupC3b1uJC1RTdiYWk8lu6JIhMR65ty/I5C67oPX2VCOTSknPtr+zkJmTh5+29Im9YJ2Q/fEvi/ji5dFERTpegaRIp2fDvqM8P9j1jR9LQR4WswmpWovppnSJyrNE78QN1vGuWXDTJFdTehISiRSpp7fdWExl8y64t+pO3sKpmFITXB6/j0qJTCIp0euQkV+En8ajRP58nYFT19I4m5jO52utY2DNFgsWoPGk3/l5VHeiqhVPIi3UG1l/8hJjOzb+P/buOzqK6m3g+HfTe++dFELvvQWQ3hGQjgURRBFBRLEhIOrPiqhIkSJNeu8ovfcOobeEkN7bZnffPxY2LEkgWRYCr8/nnD0ne/fO7DOT2Zm5c5vRY4cHjp3kh4+dNFyLKFDet2X/MSbM+Jv/vfc6dSvp15K4OTlgZmqq13SpjI8nCcmpKPPyMDczuMWjHnVmGhqVqsDTS4WtA+oiOtCr01JQpyahycnvzK6Ki0ZhYoKpowuqhLuo05JBrdK7yVHF3cHU3glMTUGlKrji/4BDp9O5eD3/d2dupr1ZcnIwIyk1f5842puSkvb4fWRhrqBRTTsWrS+8I+2z5GRvX/hvISUVF6dHFwr+2XuIr6fMYeIHQ6hTpcJTi7FCzWaMDM0fHSnvXlOStJR4HJzzHzKkpyYWqJ0oKTfPAIZ+8Rc52ZnkZGXg4OzOvMkf4OLu90TrhQd+t/b6v1sTW/sif7eqtGRMHvrd5j30u9Uxt8CySl0y/ln5xLHeV7ZaM3zL5DdbzcvTNpFKT43H3il/aPqMtARsHR6/7/dvnsmeDdPo98EsPP0Lr2VOToji2rn99Bj66xNGn+/+NVfxUK2Dia096ozCmx+q01NQpyXrDSyhir9z75rrjMLCAlMnN+x6vpO/0L0CofMnU0j54wvUSYbXjD0PNDLNwDNl0KOK06dP07VrwSo7d3d3EhISHrv8mDFjSElJ0XuNGTPm8V+syiP3xhWsKuh3GLaqUI3sK49u/mIVXglzTx/Sdxfs7Jh9+TzmHl56T1fMPH3IS0406hNxczMzygX5cfCM/tB4B89cpEpYUJHLbdp/jHHT/2bi2/1oVK3oC9/WgydQ5uXRtsFT6NyuVqGKuYlZGf3hQc3LlCvYaeuevNtXMLFzggee3Ji6eKBRq7U3f/dY1m2BdcO2pC/+DVXMTePHDpibmVLex5UDV/SH4ztwJZqq/gXnPLGztGDZ211ZPKSL7tWjVjmCXB1ZPKRLgdqGLWevkZunpn2VwtuqPnn8ZpQr48/BM5F66QdPR1IlrOiRoDbtO8q4aQuZ+M4AGlUvOGRr1bJluHU3HrU6vwB9MyYWNycHoxUgAFCpyIu+jkWofgwWoRXIu1n4EMHKG5cwsXcCiweOH1cvNGo1qnujwuTdvIypi6feb9fUzQtVatJ/tgABkJ2jISZeqXvdisklMSWPquXy+8OYmULFUGsuXH38KHQNa9hhbqZg5+Hi9Z14mszNzQgPDuTwKf2ms4dOnaNyeNHtu7fsOciE32cx7v1BNKxp3EEnHmZlbYubV6Du5ekbgr2TGxdP5w+zmZeXy5XzRwgqW/0Rayo+SysbHJzdyUxPIfLUXirVNEJtXJG/24oobxbe/0R58xKm9k4oHvzduun/bu+zqlQHhak52ScKDj9qKEsrO1w8A3Uvd59Q7BzduXY2/ztUebnciDyMX+ij9/2+TTPZve4P+rw/A5+goofMPblnBbYOroRVKbzDs0HUKlR3bmJe4Jpbvshh1fNuXdGeMx+85rp63rvmJqGKjyFl2jhSZ3yleykvniLv+kVSZ3yFOqXwEZ+EKIpBhQgnJyfu3LlTIP348eP4+j5+VBdLS0scHBz0XpaWxesEnLJ1NfaNW2LX8CXMvf1w7jkQMxc30nZoOx85vdwftzfeL7CcXaMW5FyJRBld8CY1bccmTOwccOn1JmaePlhXrolT+x6kbd9QIO+T6ts2glU7DrJ650GuRd3lx/mriElIottL2naWvy1exxdTF+ryb9p/jLHTFvJ+n85UCg0kPjmV+ORU0jMLXvhX7zxIRI1KONk/nc6C2Ye2YVm1ARZV6mPi6oX1S90wcXAm97h27G+riM7YdHhVlz/37BE0WRnYtu+PiasXZv6hWDfvSu6pfbpOXpZ1W2LdpCMZG+ahTklEYeuAwtZB7yRoLP3rV2LFsYusPHaRq3HJfL/pIHdS0ulRS9sp/5d/jvDpCm27XBMTBWGeznovF1srLM1MCfN0LtBxeuXxizQrF4CTjfGHRr2vb9umrNp+gNU7DnAtKoYf5624d+xoOwz+tmgtX/wxX5d/076jjJ06n/f7dqZSaFChx063Fo1ISc/gh3kruHEnlj3HzzJ79VZ6tGxs9Piz9m7BqmYTrGo0xtTdG9u2vTB1dCXr8HYAbFt2x77bm7r82acOoM7KwOHlgZi6+2AeVBbbNq9o54i4d/xkHdqOwsYWu3Z9MHX1xKJsFWwi2pN9cJvR4y8uU1sbHKqWw6Gq9riyKeOHQ9VyWPl7l1pMAOu2J9O9lTN1q9gS4G3BsP5e5Cg17DqSX5P4Xn9P+nUq+HS2RX1HDp7KIC2j4EATVhYKgnwtCPLVjh7k6WpOkK8Fbs5GLIQ+pHfHVqz5dzdr/93N9dvRTJq9iLvxiXRtpb2Jm7JgOeMm/6nLv2XPQcb/OpP3BrxCpbAQEpJSSEhKIT2j8P5QxqZQKGjcpj//rp7B6cP/cOfWJRZP/RQLCyuqN2ivy/f3lDFsWJQ/T0JeXi5R188Tdf08qjwlKYmxRF0/T3xM/jw6kSf3cOHkbhJib3Px9D6mTnwdd+8gakcYp21+5t7NWNeMwKqm9ndr1643Jo6uZB2697tt1R377vnzEeWcPIA6Mx37l9/U/W7t2vTUzhHxUMdqq1qNyTl/7Kk2rVEoFNRpMYA9G6Zx4dhWYqMusnrWGMwtrKhUt4Mu36qZH/Hv8h917/dt/JMdqybR8bWJOLn5kp4SR3pKHLkPDfqhUas5uXclVep3wcTUuMd89sF/sKzeCIuqDTBx9cKmZQ9MHF3IObYLAOtmXbDt9Jouf86ZQ6iz0rHr+Combt6YBYRh81I3ck7u1e57VR6quGi9lyY7E01uNqq4aG2trhAlYNAR36dPHz766COWLl2KQqFArVazd+9eRo0axYABAx6/gieQeXgPibb2OHXsiamjC7nRN7j7y3jdaEtmjs6YubrpLaOwtsGmRgMSF80odJ2qpHju/jQWl54D8f3yF/KSEkj9Z612dCYja1WvOinpmfy5agvxyamE+Hnzy6hBeN8b5z8+OY2YhPynASu27UelUvO/v5bzv7/yR6Xq0Kg2Xw7O7yx1404sJy5e47fRg40e833K80fJsrbFqmE7TOwctMP9LZmCOlX7dMnEzgEThweamilzSPt7MjatXsHh9Y/RZGWQe/6odnSmeyxrNEFhZo7dy2/pfVfW7vVk7yl66ENDtKkUTEpmDtN3niAuPZNQD2d+79sKn3tzRMSnZRKTUvKL2fX4FI7fvMvU/sYZG7worerXICU9gz9XbiY+OUV77Hw4GG/3+8dO6kPHzj7tsTNnGf+bs0yX3qFxHb4c0hcAL1dnfvv4bX6at5LeY/6Hu7MjvdpE8GrHFkaPP+fMIRQ2ttg064SJvSN5d6NImfcz6mRt7aWJvSMmTg/cwObmkDL7e+w69MP57S9QZ2WQc/qQdnSme9QpiaTM+RG7dr1xfncC6rQksvZvLTAK1LPkWLMS9f/N77RY4YdPALg1dwWnBhajxvUpWflPEhYWCt7q6YGdjQmXrmcz7rcovTki3F3MCjR/9/Ewp0KoNV/+Vngzw5BAK74ant905o1u2lq6bQdS+XX+3UKXeVItGtYhJS2dWcvWkpCUQnCALz9+Mhxvd+25PyEpmbvx+U+9V23ZiUql4oc/F/DDnwt06e2aNuDzdwc+lRgf1qzjQJS5OayYPYGsjFQCQqowaMwMvTkikhLuoDDJr1VLTYrj50+6697vXD+bnetnE1y+NkM/nwNAVlY6GxdNIjkxBhs7RyrXbknbnsMxLaT/gSFyTh8i3cYO22ad83+3c3964HfrhOkDTbI0uTkkz/4B+459cRk6FnVmOjlnDpO+VX9URVNXTyyCwkma9b1R4nyUBm3eJC83m40LxpOVkYJvcBX6jpypN0dEakK0Xv+UIzsWospTsuyP4XrratLxHSI6509Ue/X8PlISo6nW6GWjx5177ggKa1usG7fXTTaXtug33fwcJnaOmDg+MEeQMoe0BZOwbd0Lx4GfoM5KJ/fcUbJ2rDZ6bM8rjUaaMz1LCo2m5OMgKpVKXnvtNRYtWoRGo8HMzIy8vDz69u3LnDlzMDUt3mgfD7v+ZmeDlnseBP25mrRDxr3pfVbs67Qn6ZuhpR2GwZzHTCH778InYnoRWPX+iLQjmx6f8TlkX6sNcZ+9XtphGMz9q9msNzf+aFrPSntlJF3ffTEni1r5WxiJp/eUdhgGc6nciLVHn84AEM9Cx5pmxH76WmmHYRCPiXOYv/vFHcK5X2MFiV89vQd+T5vLZ9NKO4QiXb5inA76JRUaYvwJZl8EBtVEmJubs2DBAiZMmMCxY8dQq9VUr16dsLCip5AXQgghhBDiadEYPnOBMIBBhYiRI0cWSDtw4AAKhQIrKytCQ0Pp3LkzLi4uhSwthBBCCCGEeJEZVIg4fvw4x44dQ6VSER4ejkaj4dKlS5iamlKuXDmmTJnCBx98wJ49e6hQ4ekNoyeEEEIIIQTwn574rTQYVO/TuXNnWrRoQXR0NEePHuXYsWNERUXRsmVLevfuTVRUFE2aNGHEiBHGjlcIIYQQQghRygwqRHz//fdMmDABB4f8SVAcHBz48ssv+e6777CxseGLL77g6NGjRgtUCCGEEEII8XwwqBCRkpJCbGxsgfS4uDhSU7WTETk5OZGbm/tk0QkhhBBCCFEMGhSl8vqvMrg50xtvvMHKlSu5ffs2UVFRrFy5koEDB9KlSxcADh06RNmyZY0ZqxBCCCGEEOI5YFDH6mnTpjFixAh69epFXp52nGwzMzNeffVVfv5ZO9tmuXLl+PPPPx+1GiGEEEIIIYziv1wrUBoMKkTY2dkxY8YMfv75Z65evYpGoyEkJAQ7u/zZH6tVq2asGIUQQgghhBDPEYMKEffZ2dlRpUoVY8UihBBCCCGEeAE8USFCCCGEEEKI54E0Z3q2ZH5wIYQQQgghRIlITYQQQgghhHjhaTRSE/EsSU2EEEIIIYQQokSkJkIIIYQQQrzwpE/EsyU1EUIIIYQQQogSkUKEEEIIIYQQz9CUKVMoU6YMVlZW1KxZk927dz8y/86dO6lZsyZWVlYEBwczderUAnmWL19OhQoVsLS0pEKFCqxcufJphQ9IIUIIIYQQQvw/oEFRKq+SWrx4Me+//z6ffvopx48fp3HjxrRt25abN28Wmv/atWu0a9eOxo0bc/z4cT755BPee+89li9frsuzf/9+evbsSf/+/Tl58iT9+/fnlVde4eDBgwbvz8eRQoQQQgghhBDPyE8//cTAgQN58803KV++PJMmTcLf358//vij0PxTp04lICCASZMmUb58ed58803eeOMNfvjhB12eSZMm0bJlS8aMGUO5cuUYM2YML730EpMmTXpq26HQaDSap7Z2IYQQQgghnoHTl++WyveW9XciJydHL83S0hJLS8sCeXNzc7GxsWHp0qV07dpVlz58+HBOnDjBzp07CyzTpEkTqlevzi+//KJLW7lyJa+88gqZmZmYm5sTEBDAiBEjGDFihC7Pzz//zKRJk7hx44YxNrOA52p0pnZvnC7tEAy2YVZlpm4u7SgMM6Q1dBl6sbTDMNiqKWUZNim1tMMw2K/vO/DGuNjSDsMgs8Z60KhjwRPei2LP2gi6vnuptMMw2MrfwlhvHl7aYRikvTKSC1dul3YYBisX4kfGtE9LOwyD2Q6eyIDP75R2GAaZO8Gb7HWFP7F9EVh1eJs+H7+4x/7Cb/1KO4TnzjfffMO4ceP00saOHcuXX35ZIG98fDwqlQpPT0+9dE9PT2JiYgpdf0xMTKH58/LyiI+Px9vbu8g8Ra3TGJ6rQoQQQgghhBAvkjFjxjBy5Ei9tMJqIR6kUOj3pdBoNAXSHpf/4fSSrvNJSSFCCCGEEEK88Eprxuqimi4Vxs3NDVNT0wI1BLGxsQVqEu7z8vIqNL+ZmRmurq6PzFPUOo1BOlYLIYQQQgjxDFhYWFCzZk22bt2ql75161YaNGhQ6DL169cvkH/Lli3UqlULc3PzR+Ypap3GIDURQgghhBDihad+QWasHjlyJP3796dWrVrUr1+f6dOnc/PmTYYMGQJom0dFRUUxd+5cAIYMGcJvv/3GyJEjGTRoEPv372fmzJn8/fffunUOHz6cJk2a8L///Y/OnTuzevVq/vnnH/bs2fPUtkMKEUIIIYQQQjwjPXv2JCEhgfHjx3Pnzh0qVarEhg0bCAwMBODOnTt6c0aUKVOGDRs2MGLECH7//Xd8fHyYPHky3bp10+Vp0KABixYt4rPPPuPzzz8nJCSExYsXU7du3ae2HVKIEEIIIYQQLzxDJn4rLUOHDmXo0KGFfjZnzpwCaRERERw7duyR6+zevTvdu3c3RnjFIn0ihBBCCCGEECVicE3EoUOH2LFjB7GxsajVar3PfvrppycOTAghhBBCCPF8MqgQ8fXXX/PZZ58RHh6Op6fnI8eoFUIIIYQQ4mkrrSFe/6sMKkT88ssvzJo1i9dee83I4QghhBBCCCGedwYVIkxMTGjYsKGxYxFCCCGEEMIgL1LH6v8PDOpYfX+IKSGEEEIIIcR/j0E1EaNGjaJ9+/aEhIRQoUIF3Wx5961YscIowQkhhBBCCCGePwYVIoYNG8b27dtp1qwZrq6u0plaCCGEEEKUKulY/WwZVIiYO3cuy5cvp3379saORwghhBBCCPGcM6gQ4eLiQkhIiLFjEUIIIYQQwiDSsfrZMqgQ8eWXXzJ27Fhmz56NjY2NsWMqlr6dPWgT4YKdjSmRVzOZMj+am9E5Reb/dnQZqpSzK5B+6GQqX/5yQ/fe1cmM13t4UauyPRbmJkTdzeGX2be5fCPbaLFrNBoObPyN0/sWk52VindgVZr1+AI377Ail7l0cguHtkwlJf4mKlUezu6B1Gj2OhXqdNHlObl7Iaf2/k1qQpR2W7zDqNtmKGUqRBgt9vt6tXelVUNHbG1MuHQ9m2mLY7l1J7fI/F+970elsgWPlSNn0vlqSjQAbRo70qaJEx4u2sPy5p1clmxI4Ni5TKPG3raeJQ0rmWNtpeBGjIol27KJSVQXmb9uBXP6tbIukD7i11TyVPnvHW0VdG5kSYUgM8zNFMQmq1m4NYtbsUWv2xCdI2yJqGmFjZUJV6OUzN+QRnSc6pHLWFsq6PaSLTXKWWJrbUJckorFW9I5fVn7P2vXyIaa5SzxdjMlNw8u31Ky7J90YhIevV5DvNE7kE6tvbG3M+PcxTR+mnqJazcf/T/u0cmXrm198HS3JDlVyY598Uz76yq5So1unW/0CdJbJiEpl84D9hs9/p7tXLTHvrUJl25kM31xHLdiij72Jwz3pVJYYcd+BhOnao/9CiFWdGnhTEiAFS6OZnwzPZpDpzKMHntxuDSqRfAHA3GsUQkrHw+OdBvK3TX/PtMYNqxbzcrlS0hKTCAgMIiBbw2lYqUqReY/c/oks2b8wc0b13FxdaNrt560bd9R9/n+vbtZunghMXeiyMtT4ePrS+euPWj2Uktdno3r17Bx/Rpi794FICAwkJ69+1Ozdt0n3p4lJ64w90gk8RnZBLs6MKppVWr4uRea98itWN5auqtA+vLXWlHGxaFA+uYLtxiz4SBNQ3z4qXODJ461KF2b2dG0lg221iZcuZ3L3HWpRMXmPXIZGysF3VvYU6uC9nwVn6xi4cZUTl0qeK3u0MSWV1o6sHlfBgs2pho19sV7TzJnx1HiUzMI8XJldOcIagT7Pna549eiGThlKaFeriz5oJ8u/XJMAlM27ef87btEJ6XxYecm9GtSw6gxP6xbCwea17HF1tqEy7dymb0qqVj7/5XWjtSuaH3vvJ/HgvUpnIjMv59xdjChd1tHqpa1wsJcwZ34PGYsT+JalPKpbo/4/8GgQsTkyZO5cuUKnp6eBAUFFehYfezYMaMEV5Tubd3o2sqNn2beJupuDr06eDBxVBne+uQiWdmF37B99ftNzE3zS6j2dqb8Pi6MPUdSdGl2Nib88EkIpy6k88XP10lOzcPbw4L0TOPeBB75ZwbHts+mVb9vcXYP4uCWP1jx++u89tkmLKwKFnQArGwcqdvqbZw9gzE1Nefq2e1sWfgJNvauBJVvrI3fyYtGHUfh5B4AwLlDq1gz4x36jl75yAJKSXVt6Uyn5k5MnneX6Lu59Gjrwrhhfgwdd43sHE2hy3w7PRozswf2v60pkz4JZN+xdF1aQnIe81bFcydOe0PWrJ4DY4b4MvKbG48soJREi1oWNKtuwYItWcQmq2ldx5J3X7Zhwl/p5DzinJmVo2HCX+l6aQ8WIKwtYURPWy7dyuOPVZmkZWlwczQhq4j9Yai2DW1oVd+amavSuJuQR4cmtozq78QnvyWSnVv4d5mawKj+TqRmqJmyNJWkVBUuDqZ6+cMDzdl2OItr0UpMTRS83NyWkf2c+GxKArlGvJb07eZPzy5+TJwUya2oTF7tGcjP46vQ++3DZGUVXmBpGeHBkFeD+XZyJKfPp+Dva8Onw8MB+PXPK7p8V29k8P5nJ3Xv1cb92QLQtYUznZo58ev8u0THKunexoUvh/nyzvjrRR77/5txBzNT/WP/5zEB7DuepkuzsjThelQu2w6k8tEgH+MHXgKmtjaknork9l8rqLn0t2f+/bt3bmfm9CkMHvoe5StUYvPGdYz/Ygy/TZ2Fu4dngfx3Y+4w/otPaNWmHSNGjeH8uTNMmzIZR0dHGjRqAoCdvT09evXFz88fM3Nzjhzcz+Sfv8PRyYkaNWsD4OrmxoDXB+Htrd3/2/7dwtcTvuDnX6cREBhk8PZsjrzFDztOMOalGlT1cWX5qasMW7mHZa+2xtuh6IdwK19vja1F/rXV2dqyQJ7o1Ax+3nWK6r5uBsdXHO0b29KmgS0zVqZwJz6Pzk3tGP2qCx/9Elf0eccURr/qQmqGml8XJZGYosbV0YSsQvKX8TWnWS0bbsYY/8Z10/FIvlu9k09fbk61Mj4s23+KoTNWsXJ0f7ydCxbK7kvLyuGzvzdTJ9SfxHT9hxzZuUr8XB1pWTWMH1bvNHrMD+sYYU/bRnZMW5rInfg8ujZ34JM33fngh5hH7v8xA91JzVDxy4IEElNUuDqa6u1/W2sFX77twbkrOXw3O56UDDWeLmZkZD2Fk+czIn0ini2DChFdunQxchgl/P6WbixaF8u+Y9qnFT/OvM3CSeVpWteJjTsTC10mPUP/BqVJXUdyctXsPpxfiOjezp24RCU/z4rSpcUmGPekptFoOLZzLnVaDSGsaisAWvf9H9M/a8CFo+uo0rBXocv5h+k/DavR9FXOH1pF9NWjukJESOXmenkadhjByT1/E3P9hFELER2bO7N0UyIHTmhvqn+Ze5e/vg2mSW0HtuxJKXSZhwtijWvak5OrZu+x/Bupw6f1n7wuWJNAm8ZOhJexMlohoml1C7YczuHkFe0TnPlbspj4lj21ypmz93TR/2sNkJZZdIGgZS1LktPULNia/4QnMdX4T/Fb1rVm3e5Mjl3QPsmbuSqVSaPcqFvZkp1HC68ta1zdCltrE76elYTq3r8hIUX///HzAv3/26zVqfzyoTtB3uZcvGm830CPTr7MXXKTXfvjAZj48wXWzGtAqwgPVm+6U+gylco5cPp8Clt3xgIQE5vDP7tiKV9W/wZApdKQmPx0n551aObEss1JHDipPVYnz7vLnK/L0KSWPVv2Fv709OFjv9G9Y3/f8fxC6bFzmUavcTNU3OZdxG0u+CT8WVm9chktWrWlVRttn7s3B7/D8WNH2Lh+LQNef7NA/k0b1uLu4cGbg98BwD8gkMuXLrJqxRJdIaJylWp6y3Ts0o1t/27h/NkzukJEnbr6T/H7vzqQTevXEnnh3BMVIhYcvUiXSmXoWrkMAB82q8b+G3dZdvIKwxpXLnI5F2tL7K0sivxcpdbw2YZDDKlfgeNR8aQ96inIE2pd35Y1u9I5ck57jpm+PJlfP/KkfhVrth8p/LhtUsMGWxsTJsxIeOC8U/CcaGmh4O3uTsxalUKnpoU/RHsS83Ydo2udirxcrxIAo7s0ZV/kDZbsO8Xw9o2KXG7Csn9pWz0cUxMF289c0fusUoAXlQK8AJi8fo/RY35Ym4Z2rN6exuGz2v3/x5JE/vjMhwbVbNh2qPAay6a1bLGzMeHLP2J1+z8+WX//d4ywJyFZxbRlSbq0+CTjX7fE/18GFSLGjh1brHx///03nTp1wtbW1pCvKZSXuzkuTuYcO5t/Ac7L03A6MoPyoTZFFiIe1rqxCzsPpZDzQKm8XjUHjp5JZ8zbAVQOtyUhScm67Qls3pX0iDWVTErCbTJT4wgsl3/yMjO3wDekNtHXjhdZiHiQRqPh1sUDJMZeo1GnUYXmUatVXDq+ibycTLyDqhstfk9Xc1wczThxPv/CkZen4cylLMoFWxVZiHhYiwaO7Dmaprf/H2SigAY17LGyUHDhqnGakrk6KHC0NeHCjfwq4DwVXL6dRxlv00cWIizNYdwbdigUEBWnYv3+HG7H5d8cVgo248KNPN5oZ02onynJ6Rr2nMpl3xnjXdjdnUxwsjfl7JX8AlWeCiKvKwn1My+yEFEt3JIrt5X0a2dPtXBL0jLVHDydzYa9mWiKKBdZW2qnkDHmEykfTyvcXCw5dDz/96TM03DiTDKVyjkUWYg4dS6FVk09KR9mz/lLafh4WlGvlgubtt3Vy+fnY82qOfXIzVNzLjKN6XOvEX3XeM0QPV3NtMf+Bf1j/+zlLMoFWxdZiHhYiwYO7DmWXuSx/1+mVCq5cvki3V7prZderXpNLpw/W+gyF86fo1r1mnpp1WvW5p8tG8nLy8PMTP8yp9FoOHXyOFG3b/Pq64XfxKtUKvbu2Ul2djbh5SsYvj0qNefvJvNa7XJ66fUDPTkZnfDIZXvP/4dclZoyLva8Wbc8tQM89D6ffuAcztaWdKlchuNR8QbH+DjuzqY42Zty5nJ+EyTteSeXsABzth8pfLka5Sy5fEvJgA6O1ChvSVqGmv2nsli3O0PvvPNqBwdOXMzm7NVcOjU1buzKPBXnb8fyRvPaeun1wwM5eb3w8w3AqkNnuZ2Qwtd92jDjn4PGDaqEPFxMcXYw5dSl/HNZngrOX8uhbKBFkYWImuWtuHQzh9c7O1GzgjWpGWr2nchkzc403f6vUd6aU5eyGd7HhXLBliSlqti6P4Pth0unKaV48RhUiCiuwYMHU7duXYKDg422TmcHbfVucqp+W8Dk1Dw8XM0LW6SAsmWsCfKzYtLs23rpXu4WtG/mwsrN8SxeH0t4GRuG9PFBmadh275ko8SfmRoHgI2Dq166jYMbaYnRj1w2JyuNGZ83QZWXi8LEhOY9xhJYTn/m8PjoSBb91Iu8vBwsLG3o+ObvuHqHGiV2ACdHUwCS0/T3f0paHu4uxdv/YYFWBPpa8tv8uwU+C/Sx4NtRAViYK8jOUfPt9DvcfkR785JwsNXeGKc+VKOQlqnBxaHoeRfvJqqZvyWbO/EqrCwURFS3YMQrtny7IIO4ZO1NtpujCY2qWLD9WC5bDucQ6GVKt6ZW5Kng0HnjFCQc7O7Fn65/Y5+aoW0mUBR3Z1PKlzHlwOlsJi1MxtPFlH7t7DExgbW7Cn+K2LO1HRdv5BL1mL4WJeHirH2qmpis//9MSs7F08OqyOX+3R2Hk6M5U/5XDYUCzMxMWLkhivnLbunynLuYxlc/X+BWVBYuTua82jOQP76vTv93DpOa9uh2w8Xl5KA9XT587CenqXB3Kd6pNCzQkkAfS35fUPDYF5CamoJarcbJyVkv3cnZmaSkwh8QJScl4uT8UH4nZ1QqFampKbi4aM+1GRnpvNG/J0qlEhMTE4a8M5xqNWrpLXf92lU++mAYubm5WFtbM+bzcQQEBBm8PclZOag0Glxt9ZsiudhYkpBZeAHXzdaaz1rUoLynM7kqNRvO32DIsl1MfyWCmvf6UZyIimf1mev83b+FwbEVl+O9807KQ+edlHQ1bk6mRS7n7mxG+TKm7D+VxY/zEvF0NePVDo6YmChYvUP7ELBuZSsCfcz5curTKQQlZWShUmtwtdNvNuZqZ0N8WuHnvhtxSfyyfi+z3+2BmalB8/EalaOddh+npOmfi1PTVLg5F33e8XAxo4KzGXtPZPLdnHi8XM14rbMTJqaw8t80XZ4Wde3YuCeNVTvSCPGz4NVOTuSpNOw+9nzUjJbUi9sQ68X0VAsRmiIec+bk5JCTo9+xytKyYHtPgKb1nBg2IL+N8NhJN+6tWz+fopC0orRq7Mz129lcvJalvw4FXLqexV8rtBf4qzezCfC1pH1TV4MLEecPr+Hfxfk1N10GT3sg4gcUI3gLS1v6fbSK3JxMbl3cz65V3+Lo5q/X1MnZowz9PlpFdlYql09sYfP8j+jx3nyDCxJNatvzdu/8dshf/XGvqVeBcBXF3v8tGjhwIyqHS4V0Vo+6m8uIb25ga21C/er2vDfAk09/vm1QQaJWuBm9XsrvED11dWahsT/u2Lkeo+J6TP4J/Gp0FqP72tKkqjnLd2qPY4UCbt5VsXaf9v3tODVeLiY0qmJucCGiXmVLBnSw172ftDClsPBBUUjagx8rtAWNOWu1T6Bu3MnDyd6ENg1sCi1E9Gtnh7+nGd/MerIauJYRHnz4Tlnd+9HjT1PoBigevQHVKzky4JVAfpx6iXORafh5WzH8rVDiE3P5a/FNAA4czb/BvHoDzlxIZfGMurRt7sXi1beLWvUjNallz5De+U9/J/4RXWj8JTn3vFTfkRvROVy6UfQgEEJ7SDxIo+GR8xEpijifPphubW3DpN+mk5WVxamTx5g14w88vbz1mjr5+vkz6bfppKens3/vbn758X9M/O6nJypI3I9QL7zCYr4nyMWeIJf8331VH1di0rKYd+QiNf3cychV8tnGQ3zeskah/SSeVP0qVrzeyVH3/sf52vNAgWuuAjSP+OGaKCAtQ82s1SloNHA9Og9ne1PaNbJl9Y50XBxM6NfOge/+SkRpnHJ+kQocTxS4AgOgUqsZs2ATb7euR5C7cyE5nr6G1awZ2DX/u7+bU0QBS/Ho8472vK/izxVJaDRwLUqJs4Mp7ZvY6woRJgq4GpXL4s3aWtQb0Ur8PM1oUdf2hS1EiGfrqRYiivLNN98wbtw4vTRtE6luBfIePJFK5NX8g9n8XudcZ0czklLyzzyODmYFaicKY2mhIKKOE/NXFXwSmJScx62HRni6FZ1Dw5qOBfIWV0jl5ngHVdW9z8vT3gxnpsZj55h/g5KZloCNw6M7xylMTHByDwTAw688iTFXOLx1ul4hwtTMQpfHK6AyMTdPc3znXFr0Gm9Q/IdOpXPxev7N/v397+RgRtIDbf4d7U0LPKEtjIW5gka17Pl7XeFV+XkqiInT3nRfuZlDWKAlHZs58cffsSWO/fTVPK7H5Dd7u9+51cFWoVcbYWejIK0Enec1wM0YFR7O+U/hUjM0BUZ4upukplpY8WpnCnMiMpert/Nv5O+3ynC0M9F7KuhgY1KgduJBKWlqVGr9C86deBVO9qaYmqBrLwvQp60d1cpa8u2cJJLSnuyZzp5DCZy7mN/WwcJc+1TPxdmChKT8QqGzo3mB2okHvdmvDJu332XdlhhA24HaysqU0e+WZe6Sm4VeSLNz1Fy9noGfT8FRtYrr0OniH/sPPyUsjIW5gkY17Vi0vnhNLv+LHBwcMTExISlJvwCbkpxUoHbiPidnlwK1FMkpyZiammLvkN9vxsTEBG8f7Yg8wSGh3Lp5k2VL/tYrRJibm+vyhJUN59KlSNatXsHQYSMN2h4na0tMFQoSMvQfmCRl5uBiU/wCQGVvFzac1xaYbydnEJ2ayfur9uk+V9/7EdT+eTkrXm+Nv5PhfQuOX8jhyu38G1fdcW//0HnH9tHnneQ0VYHzTnRcnva8YwpBvuY42pkyfkj+dc/UVEF4oAUt6trwxriYYhfOi+Jsa42piaJArUNieiau9gU7tWfk5HL21l0uRMXy7crtgHbfajRQ48Nf+OOtl6kb5v9kQT3G0XPZXL6Vf39y/7qlvcY+sP/tTElJL/q8k5ymRqXS6O3DqNg8nB20+1+lgqQ0VYERnqJj86hTqXRG3TQG6Vj9bJVKIWLMmDGMHKl/Ura0tKTr2xcL5M3KVpOVrX+DkZispEYFO67e1J6YzUwVVA63ZfbSmMd+d+PajpibK9i2P7nAZ+cuZ+LrpX9i9/WyJDbB8OY0FlZ2eiMuaTQabBzcuRG5Fw9/bVtbVV4uUVcOF9m/oSgaNKjyHhdbcfIULTtHo7upvy8xJY9q5W24dltb4DIzhUph1vy16vFV0o1q2mNupmDnoeK1H1eg0F3ESipHCTkpD16FNKRkqAkPMOP2vRGgTE0g1M+MNXtK1nbe192U6IQHaydUeDrrV317OJmQmGr4jXh2robsXP2LRHKaigrBFtyM0Z74TU0gPMicpf+kF7YKAC7dUlKvspX2ifm9NE9XU91F/r6+be2oUc6S//2VTHzyk1cKZ2WpiHpoxKX4xBxqV3Pm0lVtvGZmCqpVcmLqX1eLXI+VpQkatf7dhFqtHQ1cUcTTOHMzBYH+Npw8V7w+OoXJztEQk1Pw2K9aTv/YrxhqzdzVjz/2G9aw0x77h407fOX/J+bm5oSEluXk8aPUb5Dfb+zE8aPUrdew0GXKla/AoYP6Q/meOHaE0LCyBfpD6NOQp3xMLaFGg/JxeR7B3NSE8p5OHLx5l+Zh+UOKHrhxl6YhxR+FKzI2GTdbbZO/IBd7lgxoqff5lL1nychV8mGzangVcnNcEtm5GrITC553KoZYcuPOvfOOKYQHWbBkS1phqwDg4k0l9atY6f1GvVxNSUpVoVLBuSu5jPk1Tm+ZQV0duROfV6DfhKHMzUwp7+fBgYs3ealyfm38gYs3aVqxYDNrO0tLlo3qp5e2ZN8pDl26xQ+vtsfXxfAHisWVnash+6GhtZNSVVQOteRGtPZYNDWF8mUs+Xtj0ee3izdyaFDNRm//e7ub6fa/Nk8u3m76vxEvdzPik59y1ZD4f6NUChGWlpZFNl8qjlVb43mlgwdRsblE382hZ3sPcnLV7DiYrMvzwZt+JCQpmbNcv8ahVWMX9h9LJS2jYAl+5ZZ4fvwkhFfau7P7cArhZaxpG+HC5L+iCuQ1lEKhoEbEAA5vnYazexBO7oEc2joNM3MrytXsoMu3ad5o7Bw9adTpAwAObZmGZ0AlHN0CUKtyuXZ2F+cPrab5K1/qltmz9ieCKjTB3skLZU4Gkcc2cPvSIbq+/afR4gdYuy2J7q1diI5Vcic2l+5tXMjJ1bDrgZuj4a96kZCcx/yHbq5aNHDg4Ml00jIK3qT26+TKsbOZxCcpsbYyoVEteyqWtWb8b8Z7crvjeC6t6lgSl6wmLllNq9qWKJUajlzIv1Ho38qK5AwNa/dqbxTb1rXgWoyKuCQ1VpYKIqpZ4OduwtLt+c3hth/PYeQrtrSqbcGxi0oCvUxpUNmCRf9kFYjhSWw9mEWHxjbEJuZxN0FF+8a25Co1HDydX4P2Zhd7ktLULP9X2zlu+5EsWtSxpndbO/49mIWnqyntG9ny76H8p3P92tlRr7IVkxelkJ2j0fUfycpRG7WpwdI1UfTvEcDt6ExuRWcx4JUAcnJUbNmZX9P02Yhw4hJymTb3GgB7DyXQs4sfF6+mc+5iGr7e1rzZtwx7DiXohnF9541g9h5K4G5cDs6O2j4RtjambPz38Q8WSmLd9mS6t3LmTmwud+KUdGvtQo5Sw64j+TdT7/X3JDElj/lr9GvbWtR35OCpjEKPfSsLBV7u+bVWnq7mBPlqh5eOT3q2F3RTWxtsQwN0723K+OFQtRy5iSlk3yq6M6qxdO7anUk/fktoWFnCy1Vg86b1xMfF0qaddt6HubP/JCEhnhGjPgagTbuOrF+7mpnTp9CqTXsiL5zjny0b+WD0p7p1Llu8kNCwsnh5+5CXl8fRwwfZ/u9WhrwzXJdn3pw/qVGrDm7uHmRlZrJ713bOnD7J2PHfPNH29K1Zls83HqK8pzNVvF1ZcfoqMWmZdKuqvYn9dfdpYtOzmNC2DgALjl3Cx8GGEFcHlCo1G87f5N9LUXzfsT4AlmamhLrp38zaW2qPnYfTjWXz/gw6NrHjbkIeMQkqOkXYkavUsP9U/vntrW6OJKWqWbpV+1vYdiiDlvVs6NfOga0HMvB0NaNjhB1bDmjPO9m5mgJPwXOUGtIzC6Y/if5NavDp35up4OdJ1SBvlh84zZ2kNHrU18478sv6PcSmZDCxT2tMTBSEeeu3CHCxs8bS3FQvXZmn4spd7e9bqVITm5LBhahYbCwtCHBzMlrs923am07nZg7EJOQRE59H52YO5Co17DuRfw5/+xVnElNUuqZJWw9k0KqBHQM6OrF5XzpermZ0bmrPpn35D5w27knjy7c96NzUngOnMwnxs6B5HVtmrjDeYDLi/7dSKUQ8qWUb47G0MOGdfj7Y2Wonm/vsx2t6c0S4u5gXGCfe19OCSmVt+fSHa4Wu99L1LL76/QavdfOiTycPYuJymfZ3NDsOJBs1/lotBpGnzOHfpePIyUzBK7AqLw+dpVdjkZZ0B4Ui/8m2MjeTbUvHkZYcg5m5FS4ewbQZ8D3hNdrp8mSmxbN53mgyUmKxsLbHzSecrm//WaDz9ZNauTUJSwsTBvfywM7GhIvXs/ny19t64+S7O5sVeHrs42FOhVAbxk4uvI26k4MZ77/mhbODKRnZam5E5TD+tyhOXjBe28x/juRibqbgleZW2FgquB6j4veVmXpzRDg7mKB5oHuWtaWC3i9ZY2+jIDtXw+04NZOWZXLjbn6em3fVzFiXRaeGlrSpa0lCqpoVO7M5EmncG8CNezOxMFPQr509ttYmXL2t5Md5yXpjhbs4mvLgrk9KVfPj/GR6tbZn/NvWJKWq+edgJhv25u/X5rW1Ty8/fk2/ycjMVansPWm8EY4WLL+FpYUJI98Ow97OnHMXUxnxxSm9OSI83a304v9r8Q00GhjUrwzurhYkpyrZeyiB6fPyf8furpZ8Oao8jg7mJKcqORuZyuBRx7kbZ9y+Byv/ScLCQsFbPbXH/qXr2Yz7LUr/2HcxK/AUVXvsW/Plb4U/kAgJtOKr4X66929003ag3XYglV8LGYDgaXKsWYn6/87Tva/wwycA3Jq7glMDxzz1728c0Yy0tFQWL5xHYmIigUFBfDHuGzw8tX2zkpISiI/LL3R6ennzxfivmTl9ChvWrcHF1ZU3B7+rG94VIDs7m6lTJpMQH4eFhSW+/v6MGDWGxhHNdHmSk5OY9MO3JCYmYmtrS2CZYMaO/6ZA5+uSah3uT0pWLjMOnCc+I5sQVwcmd22Ej4N21ML4jGxiHmhuo1Sp+XnnKeLSs7A0MyXYzYHJXRrSKNj7ieJ4Eut3Z2BhpuDVjo7aSS5v5/LdX/pz07g6mqJ54JqbmKrm+78S6dPWga/ecScpTcWW/Rms2/1sR/5pUz2clMxspm89QFxqJqHervz+Zmd87k3cF5+aQUxyyWoHY1PT6fnTQt37v3Yc5a8dR6kV4svMoT2MGj/A2p1pWJgreL2zs3ayv1u5fDNTf44OVyczvfNmYoqKb2fG06+DI98O9yQpVcWmvems2Zn/wOPqbSU/z0ugZxtHur7kQFxSHvPWprD3hHEffj1LMmP1s6XQFNX7uQgqlYo9e/ZQpUoVnJ0f3fGoUqVKbNy4EX//4rUhbPfG6ZKE8lzZMKsyUzeXdhSGGdIaugwt2JTsRbFqSlmGTXpxm4j8+r4Db4wreZ+P58GssR406vj0J1t6WvasjaDru5dKOwyDrfwtjPXm4aUdhkHaKyO5cMWwTu/Pg3IhfmRM+/TxGZ9TtoMnMuDzp1+z9DTMneBN9ro/SjsMg1l1eJs+H7+4x/7Cb/0en6mU7DtfdBO7p6lBefvHZ/p/qMQ1EaamprRu3Zrz588/thBx5swZgwMTQgghhBCiuKRj9bNl0CDIlStX5urVojtCCiGEEEIIIf7/MqgQMXHiREaNGsW6deu4c+cOqampei8hhBBCCCGeJQ2KUnn9VxnUsbpNmzYAdOrUSW8CII1Gg0KhQKUy3iy3QgghhBBCiOeLQYWI7du3GzsOIYQQQgghxAvCoOZMERERmJiYMGPGDD7++GNCQ0OJiIjg5s2bmJqaPn4FQgghhBBCGJFaUzqv/yqDChHLly+ndevWWFtbc/z4cXJytGOxp6Wl8fXXXxs1QCGEEEIIIcTzxaBCxFdffcXUqVOZMWMG5ub5s6w2aNCAY8eOGS04IYQQQgghikM6Vj9bBhUiIiMjadKkSYF0BwcHkpOTnzQmIYQQQgghxHPMoEKEt7c3ly9fLpC+Z88egoODnzgoIYQQQgghxPPLoELE4MGDGT58OAcPHkShUBAdHc2CBQsYNWoUQ4cONXaMQgghhBBCPJJGoyiV13+VQUO8jh49mpSUFJo1a0Z2djZNmjTB0tKSUaNG8e677xo7RiGEEEIIIcRzxKBCBGhnrf700085d+4carWaChUqYGdnZ8zYhBBCCCGEKBbNf3i41dJgcCECwMbGhlq1ahkrFiGEEEIIIcQLwKA+EUIIIYQQQoj/rieqiRBCCCGEEOJ5oP4Pz9lQGqQmQgghhBBCCFEiUhMhhBBCCCFeeP/l4VZLg0Kjkb7sQgghhBDixfbPqZxS+d4WVSxL5XtL23NVE5F+cG1ph2Awu7odydy9tLTDMIhN4x5k7l1e2mEYzKZhNzJ3LirtMAxmE9GLvefSSzsMgzSsYMeVq1dLOwyDhQQHk3h6T2mHYTCXyo24cOV2aYdhkHIhfqw3Dy/tMAzWXhnJiVaNSzsMg1Xbspvs9VNLOwyDWLUfws6zmaUdhsEiKtq88Pc7zyt5LP5sSZ8IIYQQQgghRIlIIUIIIYQQQghRIs9VcyYhhBBCCCEMoZEhXp8pqYkQQgghhBBClIjURAghhBBCiBeeWjpWP1NSEyGEEEIIIYQoESlECCGEEEIIIUpEmjMJIYQQQogXnsxY/WxJTYQQQgghhBCiRKQmQgghhBBCvPBkxupnq9g1EZMnTyY7OxuAmzdvopH/lBBCCCGEEP9JxS5EjBw5ktTUVADKlClDXFzcUwtKCCGEEEKIklCjKJXXf1WxmzP5+PiwfPly2rVrh0aj4fbt27qaiYcFBAQYLUAhhBBCCCHE86XYhYjPPvuMYcOG8e6776JQKKhdu3aBPBqNBoVCgUqlMmqQQgghhBBCiOdHsQsRb731Fr179+bGjRtUqVKFf/75B1dX16cZmxBCCCGEEMUi3XWfrRKNzmRvb0+lSpWYPXs2DRs2xNLS8pH5//77bzp16oStre0TBSmEEEIIIYR4fhg0xOurr75arHyDBw+mbt26BAcHG/I1RVryz17mbdhBfEoawb6ejOrbmerhhX/HtsOnWbZtH5E3o1Eq8wj29eKtrq1oUCVcL9/CTbtYtm0/MQlJONnb8lLtKrzbox2WFuZGjR1gyfaD/LV5N/HJ6YT4eDCqVztqlA0qNO/xS9f5ZdkWrsfEkZ2rxNvViW5NatOvVUNdnhW7DrNu/wkuR90FoHygD8O6tqJSsJ/xY992gL827SY+OY0QXw9G9W5PjbJlCo/94nV+WbaJ63ceiL1pHfq1aqTLcyXqLlNW/cP561HcSUhmVK/29H1g24we/45D/LV5L/Ep6YT4uDOqZ1tqhAUWHv+lG/yyYivXY+K18bs40a1JTfq1bKDL8++xc8zcuJtbsYnkqVQEeLjSv2UDOtSvapR4NRoNqxdPZ+eWFWRmpBEcVol+b32Eb0DII5c7sv9fVi78g7iY27h7+fFy36HUrNdc97lKlcfqRdM5sGsjKckJODq70ahZBzr0eBMTk4LjLfz1x0R2bllBrzc+oFXHPsWKfd26dSxftozExEQCAwN5a/BgKlWqVGT+06dOMWPGDG7cuIGrqyvdunenffv2ennS09P566+/2Ld3L+np6Xh5efHmm29Su04dADIzM5k3dy779u8nJTmZkJAQBg8eTNnw8MK+8okt37SNBWs2k5CUTBl/X95/rRfVKpQtNO+OA0dZsWUHl67fJFeZR7C/DwNf6Uy9akXvE0NtWLealcuXkJSYQEBgEAPfGkrFSlWKzH/m9ElmzfiDmzeu4+LqRtduPWnbvqPu8/17d7N08UJi7kSRl6fCx9eXzl170Oyllro8G9evYeP6NcTe1Z6HAgID6dm7PzVr1zX69hXFpVEtgj8YiGONSlj5eHCk21Durvn3mX1/UVw7dsGjR2/MXVzJvnGdqD8mk3HmVJH5nZu3xKNHHyx9/VBlZJB65CDR039HlZaqy2Nqa4fX64NwahiBqb0duTF3iJr2O2mHDzz17Vm89yRzth8hPjWDEC9XRneJoEYxrjfHr0Ux8PelhHq5sWRUv6ce530ajYa1i6exe+tyMjPSKBNWiT6DxuDziPNo9M0rrF40hZtXzpMQd4dXXh9Fi4599fJcPHuULavncuPKOVKS4nn7o5+oXreZUWN/0e93njWZbO7ZeqrzRDyNYWC3HDjBjwvW8PGrL1MtLIjl2w8w7Ic/WfrNh3i7ORfIfyzyKnUrleWdHm2xt7Fmze7DjPh5Fn+NfY9yQb4AbNh3jF+XbuCLga9QNSyIGzFxfDljMQAf9O1s1Pg3HzrN94s2MKZvR6qFBrB812He/WUuy8e/h7erU4H81pYW9Gxel7J+XlhbWnD80g2+mrcaa0sLukVo+6UcibxGmzpVqBoSgIW5GX9t2s3bP89h+fj38HB2MGLsp/j+7/WM6d+JaqGBLN9xiHd//ovlX73/iNjrU9b/fuzX+eqvVVhbWNCtqfamLztXiZ+7Cy1rVeLHRRuMFmuh8R8+w/eLNzGmT/t7+/4I706ez/Iv3yk6/mZ1KevnibWFOccv3+Sr+Wu1+75JLQAcba15s10TgrzcMDc1ZffpSL78axUuDrY0qBj6xDFvXPkXW9YsYOCwL/H0CWDdspn88OVQvv59BdbWhdfwXb5wiqk/jKFrnyHUqNuMYwe3M/WHj/n465mElK0MwIYVf7Fj8zIGvjcO34AQrl8+x8xfx2FtY0fLhwoJxw5u5+rFMzi5uBc77p07dzJ92jSGvvMOFSpUYOOGDXzx+edMnTYNDw+PAvljYmL44osvaNOmDaM+/JBz584x5fffcXR0pFEjbaFTqVTy6Sef4OTkxCeffoqbmxvxcXFY29jo1vPLL79w4/p1Ro0ahaurK9u2beOTTz5h6rRpuLm5FTv+4vhn7yEmzVnEh2/2o0q5UFZu3cnIryex8OcJeLkXbOp5/PxF6lSpwJA+L2Nva8O6bXv48NvJ/Pn1p4QHF16QNcTunduZOX0Kg4e+R/kKldi8cR3jvxjDb1Nn4e7hWSD/3Zg7jP/iE1q1aceIUWM4f+4M06ZMxtHRkQaNmgBgZ29Pj1598fPzx8zcnCMH9zP55+9wdHKiRk3tecjVzY0Brw/C29sHgG3/buHrCV/w86/TCAgMMtr2PYqprQ2ppyK5/dcKai797Zl85+M4RTTHd8h73P71JzLOnsatfSeCJ37PhTf7o4yLLZDftmJlAj78lKhpv5J6YB/mrm74DR+F/8iPuD7uUwAUZmaEfPsTyuRkrk/4nNz4WCzcPVBlZT717dl0PJLvVu3g027NqVbGh2X7TjN0+ipWfjQA70dcb9Kycvhs4WbqhAWQmPb043zQ5pVz+GftfF4bNg5P70DWL5vBz+OGMOG3VVgVcR7NzcnG3dOPmg1asmTWj4XmycnJwi+oLA2ad2Lqd6OMHveLfr8j/v974Wasnr9pJ50j6tC1aV3K+Hoyql9nPF2cWLZtf6H5R/XrzKvtm1ExOIAAL3fe7dGOAC83dp04q8tz+vJ1qoYF0bZBDXzcXahfOZzW9apx7tpt48e/dS9dGtXk5Sa1CPbx4MNe7fFydmTpjkOF5i8X4EPbulUJ8fXEx82Z9vWr0aBiGMcvXdfl+XrQK7zSrC7hAd6U8Xbn81e7oNFoOHj+inFj37yHLo1r8nKT2trY+3TAy8WRpdsPFh57oA9t6z0Ye3UaVNKPvWIZP0a80pY2datibmZq1HgLxL91H10aVeflxjUJ9nbnw55t8XJ2YOnOw4XHH+BN2zqVCfHx0MZfryoNKoZy/NINXZ5a4WVoXr08wd7u+Hu40Oel+oT5enL88o1C11kSGo2GresW0qH7G9Ss3xy/wFAGvjeO3JxsDu7aVORyW9ctpELVurTv9gbefmVo3+0Nylepw9a1f+vyXIk8RbU6TalaqzFuHj7UatCCStXqcf3Keb11JSXEsmDGd7w14itMTYv/zGHlypW0atWKNm3aEBAQwOAhQ3B3d2f9+vWF5t+wfj0eHh4MHjKEgIAA2rRpQ8tWrVixfLkuz5YtW0hLS+PzL76gYsWKeHp6UrFSJV1NZ05ODnv37OGNgQOpXLkyPj4+9OvXDy8vryK/90n8vXYLHZs3plOLJgT5+TDi9d54uLqwYsuOQvOPeL03/bq0pUJoGfy9PXm7bzf8vTzZc/SkUeNavXIZLVq1pVWb9vgHBPLm4Hdwc/dg4/q1hebftGEt7h4evDn4HfwDAmnVpj0vtWzDqhVLdHkqV6lG/QaN8A8IxNvbh45duhFUJpjzZ8/o8tSp24Batevi6+ePr58//V8diJWVNZEXzhl1+x4lbvMuLo6dRMyqrc/sOx/HvVtPEjetJ3HTOnJu3SBq6q8o42Jx69i10Pw25SuSezeG+FXLyY25Q8bZ0ySsX4NN2fynyS6t22Nq78C1L8eQce40yti7ZJw9TfZV457zCzNv5zG61q3Ey/UqE+zpyuiuTfFysmfJ3qJrVgAmLP2HtjXKUTXQ+6nH+CCNRsM/6xbSrttAatR7Cd/AUF5/b8K98+jGIpcLCqtI91dHUKdRG8zNC39CX7lGI7r0eYca9V56KrG/6Pc74v+/F6oQoczL48L1KOpV0m8uUK9yWU49cGP6KGq1moysHBxt859eVitbhvPXb3Pmyk0AbscmsPfkBRpVLW+02EEb//kb0dR/6Al1vYqhnLz33Y9z4WY0J6/cLLIJEWif7uepVDjaWj9RvA/Kjz1ML71exVBOFvOG+cKNaE5evkmN8KJjf1qUeXmcv3mH+hUe2vcVQjh55Vax1nHh5h1OXrlVZNMzbcHtKtfvxlMzrPA8JRF3N4qUpAQqVqunSzM3tyC8Yk0uXyj6xvNK5CkqPbAMQKVq9bkSmb9MWPlqnD91iJgo7f/u5rWLXDp/gso185uSqdVqZkz6nDad+z+2+dSDlEolly9dokaNGnrp1WvU4Py5wm8oz1+4QPWH8tesUYNLly6Rl5cHwMEDByhfvjxTfv+dPr178/aQISxetEg3GpxKpUKtVmPx0AXfwsKCc2fPYkxKZR6RV29Qp2pFvfS6VStwOvJysdahVqvJzM7Gwc54fcaUSiVXLl+kWo1aeunVqtfkwvnC98GF8+eoVr2mXlr1mrW5fOmibt8/SKPRcPLEMaJu36ZipcqFrlOlUrFr5zays7MJL1/BwK158SnMzLAJK0vaMf2HRGlHD2NbofBmbBnnzmDu5o59be1v2MzJGafGTUk9mH/j6Fi/IRnnz+I3bCQVF68mfPpfePTqD4U0RTQmZZ6K87fvUr+sfs1Z/fAATl6PLnK5VYfOcjshhSGt6hWZ52mJvxtFanI8FarV16WZm1tQtmJNrkYatwBvTC/6/U5pUWtK5/Vf9VSbMxlbcloGKrUaV0d7vXRXB3sSUtKKtY75G3eSnZNLy7r5bdZb16tOUmoGA7/6HQ0aVCo13ZvX5/WOzR+xppJLSs9EpVbj4mD3UPy2JKSkP3LZ1h9+R1JaBiqVmsGdmvNyk1pF5p28fAseTg7UrVD8G7/HSUq7F7vjw7Hbk5By6ZHLtv7g2/zYO7/Ey00KDg/8tOXve/0bNlcHOxJSH7PvR/9IUvq9+Ds25eXG+jdcaZnZtP7oR5TKPExMTBjTpz31jLDvU5MTAHBw0m8a4+DkQkLcnSKXS0lOwMHJpcAyKUkJuvftXn6NrMx0Ph3WDRMTE9RqNS/3HUq9xm10eTaunIOpqSktOvQuWdypqajVapyc9avbnZ2cSEpKKnSZpKQknJ2c9NKcnJ1RqVSkpqbi4uJCTEwMJ0+epFmzZowbP57oqCimTJmCSqWiT9++2NjYUL58ef7++2/8AwJwcnJi586dREZG4uPjU6JteJzktLR7vwf95hvOjo4kJp8pYil9C9duISs7h5caGO/3kJqaot33Tvr73snZmaSkxEKXSU5KLPC/cnK6v+9TcHHRHn8ZGem80b8nSqUSExMThrwzvEBh5fq1q3z0wTByc3OxtrZmzOfjCAgIMtr2vWhMHRxRmJqhfOi4VyYlYe/sUugymefOcON/Ewj6dBwmFhYozMxI2beb279P0uWx8PbBrpoXSdu2cvWzD7H09cfv3REoTE25u2DOU9uepIwsVGoNrvY2eumu9rbEpxX+MOlGXBK/rNvD7Hdfwcz02T+3TE2OByjknOj6yPNoaXvR73fEf0OpFCJycnLIycnRS3vcSE8PerjbjAZNwcRCbNp/nGkrt/DT+6/j4pD/wzxy/jKz1v7Lx6++TKWQAG7djeeH+atxW7WVQV1aPmKNhlE8FKtGUzDtYbNGv0lmTi6nr95i8vIt+Hu40PaBE8N9czbuZtPBU8z4cCCWRVTBPgnFQztaOzfIo5eZ9fFb2tiv3GLysk34e7jStp5xOh6XVKHxP+bgmTX6DTKzczl97RaTV/yjjb9O/hNYWysLFn0+hKycXA6ev8qPSzfj5+5MrRLWuOzfuYG5U7/WvX//01/uxfyQe/OxPJKi4HY+mHZozxb279zIWyMm4hsQzM1rF/l75o84ObvTsHlHrl85z9Z1ixj744LHf1eRIRR2rDxiXYXF/AC1RoOTkxPD3nsPU1NTwsLCSEhMZPmyZfTpq+3wOGrUKH7++Wf69+uHiYkJoaGhNG3alMuXi1c7UFIFN0dDcU5GW/YcZOaS1fzvo2EFCiJPIy7tv7/ouAr8Bu7t+wfTra1tmPTbdLKysjh18hizZvyBp5c3latU0+Xx9fNn0m/TSU9PZ//e3fzy4/+Y+N1P/+mCBFBg3Entv6Lwx5eWAUH4DR1OzII5pB05hLmLKz6DhuI/fBS3fvrfvRWYkJeczK1J34NaTdali5i7uuHRvfdTLUTox5+vqOuASq1mzPyNvN2mHkEeBdvwPw0Hd25g/rSvdO/f/XTyvb9Kfu5/Hrzo9zvPmgzx+myVuBChUqnYs2cPVapUwdn50SeFwMDAQtsSfvPNN4wbN04vbezYsYxqW7NA3gc52dtiamJC/EOl8MTUdFwd7ItYSmvLgROMn7mE/73bn7oPVQ/+sXwz7RrUoGtT7SgiYf7eZOfk8tXsZQzs9FKho9UYwtnOBlMTkwK1DolpGQVqJx7m6659ihLm50VCajrT1mwvUIiYu3kPMzfsZOoHr1PW38soMetit78f+0P7Pi29hLGnMW31v8+8EKHb96mF7ftHNyfxvdeBLczPk4TUDKat3a5XiDAxMSHAQ/u0Ntzfm2sx8czauLvEhYhqdSIILpu/3jxlLqCtWXiwU3NqShIOjoU/xQRwdHIl9YFaB4C0lCQcH3gSt+SvX2j38mvUbdwaAL/AMBLi7rB+xWwaNu/IxXPHSUtJ5MNB+aMjqdUqFs/5ma1rF/L99HVFfr+DgwMmJiYkJeo/+U5OScHpodqG+5ydnQvUUqQkJ2NqaoqDg/Ym28XZGTMzM0xN8/vO+Pv7k5SUhFKpxNzcHG8fH777/nuys7PJzMzExcWFb775Bi8v4/4enOzttcdTcqpeelJKKi5Ojy4U/LP3EF9PmcPED4ZQp4pxm/o4ODhq932BfZlUoHbiPidnlwK1FMkp2n1v75C/LSYmJnj7aDtnBoeEcuvmTZYt+VuvEKH9H2jzhJUN59KlSNatXsHQYSONsXkvHFVqChpVHuYu+r9XMydn8oqolfPs1Y+Ms6eJW6rtw5R97Qq3s7MI+3kKd+b8SV5iAnmJCWjy8kCt1i2XffM65q6uKMzMtJ89Bc621piaKIhP1e8YnZieiaudTYH8GTm5nL11lwtRsXy7YjugfRig0UCNUZP4Y/DL1A0LMGqMVetEUKZsflOxPKUS0NbsPngeTUtJLFA78Tx50e93xH9DiY8WU1NTWrduTXJy8mPznjlzBn9//wLpY8aMISUlRe81ZsyYx67P3MyMckG+HDxzUS/94JmLVHlEG/RN+4/z5YxFTHy7L42rFbxoZ+fmYmKiX7Q3MTEBjaaIZ0WGMTczo3ygDwfO6T8VPXDuMlVDin8i1Wgg96GLxF+bdjNj3XZ+f/9VKt4bhcGYioz97GWqhhZ/ZJnCYn8WzM3MKB/gzYFz+h0PD5y/StWQgsdoUTQaDbl5j56RvTh5CmNtbYunt7/u5eMfjKOzK+dO5ndcz1MqiTx7lNByRRfCQsKrcPakfmf3MycOEBKev0xuTnahx7zmXuPOBhHtGPfzIr78aaHu5eTiTpvO/Rk59tGj3pibmxMaFsbx48f10o8fO0b5CoXfNJcvV47jx47ppR07doywsDDMzLTPOipUrEh0dDTqB26coqKicHFxKfCwwsrKChcXF9LS0jh29Cj16hm3Lba5uRnhwYEcPqXfz+DQqXNUDi96VK4tew4y4fdZjHt/EA1rGr8gbW5uTkhoWU4eP6qXfuL4UcqVr1joMuXKV+DEw/mPHSE0rKxu3xdOo7tBKzqLBuXj8vw/psnLI/PSRexr6DdZs69Rm4xzhTd7M7G0RPPAMQ4UeJ9x9jSWPr56VQKWvv4oE+KfWgECwNzMlPJ+nhy4qN906cDFm1QNKthk0M7SkmUf9mfxB/10rx71qxDk4cziD/pROcD4naytrG3x8A7Qvbz9g3FwcuPcyfyhb/OUSi6ePUpweOnUiBfHi36/U1o0KErl9V9lUHOmypUrc/XqVcqUMayDrKWlZaHNl4pzqenXJoLPp/1NhTL+VAkNZMWOA8QkJNO9ufYm4dclG4hLSmH8YG077k37j/PF9L8Z1bczlUMCiL/35NDSwhx7G23H4ybVKrBg0y7CA33vVe8l8MfyTTSpXhFTI5fK+7VsyGczl1EhyJcqwf6s2HWEmMQUujfVXmQmL99CbHIqXw3sDsDibQfwcnEiyFs7POWJSzeYt2UPvZrn3xTN2bibKav/4etBr+Dj5qR7cmFjaYGNVfGbiT029taN+GzGUm3sIQGs2Hn4Xuza4VonL9tMbFIqXw3qoY393/14uToR5K19+nPi4g3mbd5Nr5fyO7gp8/K4Gh17728VscmpRN6MxtrSkgBP486I3q9lAz6btYIKgT5UCXlg398bKnfyiq3EJqfx1Rsva+PfflC7773u7fvLN5m3ZR+9muePez9z4y4qBvri5+6MMk/FnjOXWL//JGP6dnjieBUKBS079GHdsll4ePvj6R3A+uWzsLC0om6T/L4LM375AmcXd7r3HwZAyw69+fbTQWxYMYfqdSI4fmgn508d5OOvZ+qWqVa7MeuWzcLFzQvfgBBuXL3A5jULaPySdog/Owcn7Byc9OIxNTXD0dkNb9+gx8betWtXfvzhB8LCwihXvjybNm4kLi6Odu3aATB79mwSEhIYNUo7LGK79u1Zu3Yt06dPp02bNlw4f54tW7Yw+qOPdOts3749a9esYdrUqXTs1Ino6GiWLF5Mp06ddHmOHj2KRqPBz8+P6OhoZs2cia+fHy1btSrZzi+G3h1bMe7XPykXHETl8BBWbd3F3fhEuraKAGDKguXEJSQx9r03AW0BYvyvMxnxei8qhYWQkJQCaM9FdrYFn+IaqnPX7kz68VtCw8oSXq4CmzetJz4uljbttPM+zJ39JwkJ8YwY9TEAbdp1ZP3a1cycPoVWbdoTeeEc/2zZyAejP9Wtc9nihYSGlcXL24e8vDyOHj7I9n+3MuSd4bo88+b8SY1adXBz9yArM5Pdu7Zz5vRJxo7/xmjb9jimtjbYhuY/kLEp44dD1XLkJqaQfat02r/HLV9MwOjPyLx4gYxzZ3Ft3wlzDw/i160CwPuNwZi7unHz+4kApB7Yh/+I0aR36ELakYOYu7jh+/YwMi6cIy9RW8MYv24Vbp274fv2cOJXL8fS1w/P3v2JX7XsqW9P/4gafLpwExX8Paka5M3y/ae5k5RGjwbaeUh+WbeH2NR0JvZpg4mJgjBv/aGVXexssDQzK5D+tCgUClp06MPG5TPxvFew2Lhi5r3zaFtdvlm/fIaTqwcv93sP0BY07ty+qv07T0lyYiy3rkViaWWNh7f2GMvOyiQuJn9gjvjYKG5di8TGzgFX9ycvIL3o9zvi/z+DChETJ05k1KhRTJgwgZo1axaYkdrBwfhtfO9rVa8ayekZzFi9lfjkVEL8vJj8wUC83bTVkvHJqcQk5FcTr9i+H5VKzf/mruR/c1fq0js0qsW4t3oBMLBzCxQKBVOWbSIuKQUnezuaVK/AO93bYmyt61QmJSOT6Wu3E5+SRqiPJ78O74+Pq7apQXxKGjEJybr8ao2GX1dsISo+CTNTE/zcXRj2civdjS/Akh0HUeap+PCPv/W+a3DHZgzpbLyh51rXqUJKeibT12zTxu7rya/vv4qP2wOxJz4U+/LNRMXdj92VYd1b0z2iji5PXHIavb7Mf7I9d9Nu5m7aTc3wMvz50SCjxQ7QunYl7b5fv/Pevvfg12F98bk3R0R8SjoxiSn68a/8R7vvTe7v+xZ0f6BTe3aOkq8XriM2KRVLc3OCvNz4amA3Wtc2zgRibbu+Sm5uDvOnf0tGunayuQ/G/q43R0RiXAwmDzyRDC1XlSEffM2KhVNY+fcfeHj6MeSDb3VzRAD0GTSalQv/YP70b0lNScLJ2Y2mrbrR6RXj7POIiAjS0tJYuHAhiYmJBAUFMW78eDw9tfMUJCUmEhebP0a+l5cX48ePZ/r06axbuxZXV1cGDxmimyMCwN3dna8mTmT6tGm8M3Qorq6udO7cme49eujyZGRkMGf2bOLj47G3t6dho0a8+uqrj3mibpgWDeuQkpbOrGVrSUhKITjAlx8/GY63u/bmKCEpmbvx+c2EVm3ZiUql4oc/F/DDnwt06e2aNuDzdwcaLa7GEc1IS0tl8cJ52on+goL4Ytw3eNzf90kJxD8wP4GnlzdfjP+amdOnsGHdGlxcXXlz8Lu6OSIAsrOzmTplMgnxcVhYWOLr78+IUWNoHJE/sVZychKTfviWxMREbG1tCSwTzNjx3xTofP00OdasRP1/5+neV/jhEwBuzV3BqYGPr+1+GpJ3bsPUwQGvvq9h5uJK9o1rXP1sNMpY7aR85i6uWDwwf0fi1o2Y2Njg1ullfN96B1VGOmknjnHnzz90eZRxsVwZMxLfIcMInzYbZXw8cSuXEbtkQYHvN7Y21cNJycxm+paDxKVmEOrtyu+DuuDjor3ux6dlEJNUvI6/z0rrrq+Rm5vDgunfkJmRSpmwSrz/xR96c0QkxsegeOAmOjkpjgkf9NK937J6LltWz6VsxZqMmvAnADeunOPHL/LPmUtna+eTqN+sI68PG//Ecb/o9zvi/z+FxoAZ4R5sM/dgZ737HSfvD7lYUukHCx/H/EVgV7cjmbuXlnYYBrFp3IPMvcsfn/E5ZdOwG5k7F5V2GAaziejF3nOPHiHqedWwgh1Xrl4t7TAMFhIcTOLpPaUdhsFcKjfiwpUXc3z3ciF+rDd/OjOJPwvtlZGcaNW4tMMwWLUtu8leP7W0wzCIVfsh7Dz7bCesM6aIijYv/P3O82rZQfXjMz0F3ev+N2txDHo8t337dmPHIYQQQgghhHhBGFR0ioiIwMTEhBkzZvDxxx8TGhpKREQEN2/e1Bs5RQghhBBCiGdBoymd13+VQYWI5cuX07p1a6ytrTl+/Lhuzoe0tDS+/vrrxywthBBCCCGEeJEZVIj46quvmDp1KjNmzNAbWrFBgwYce2iYRiGEEEIIIcT/LwYVIiIjI2nSpEmBdAcHh2LNHyGEEEIIIYQx/X9szpSUlET//v1xdHTE0dGR/v37P/JeW6lU8tFHH1G5cmVsbW3x8fFhwIABREdH6+Vr2rQpCoVC79WrV68i1lo4gwoR3t7eXL58uUD6nj17CA4ONmSVQgghhBBCiAf06dOHEydOsGnTJjZt2sSJEyfo379/kfkzMzM5duwYn3/+OceOHWPFihVcvHhRb06l+wYNGsSdO3d0r2nTppUoNoNGZxo8eDDDhw9n1qxZKBQKoqOj2b9/P6NGjeKLL74wZJVCCCGEEEIYTK35/zV79Pnz59m0aRMHDhygbl3tRLczZsygfv36REZGEh5ecJhsR0dHtm7dqpf266+/UqdOHW7evElAwAMTctrY4OXlZXB8BhUiRo8eTUpKCs2aNSM7O5smTZpgaWnJqFGjePfddw0ORgghhBBCiBdJTk6ObpCh+ywtLbG0tHyi9e7fvx9HR0ddAQKgXr16ODo6sm/fvkILEYVJSUlBoVDg5OSkl75gwQLmz5+Pp6cnbdu2ZezYsdjb2xc7PoNnx5g4cSLx8fEcOnSIAwcOEBcXx4QJEwxdnRBCCCGEEAYrrT4R33zzja7Pwv3XN99888TbExMTg4eHR4F0Dw8PYmJiirWO7OxsPv74Y/r06YODg4MuvW/fvvz999/s2LGDzz//nOXLl/Pyyy+XKD6DaiLus7GxoVatWk+yCiGEEEIIIV5YY8aMYeTIkXppj6qF+PLLLxk3btwj13n48GEAFIqCTbQ0Gk2h6Q9TKpX06tULtVrNlClT9D4bNGiQ7u9KlSoRFhZGrVq1OHbsGDVq1HjsuuEJCxFCCCGEEEL8l5W06dK777772JGQgoKCOHXqFHfv3i3wWVxcHJ6eno9cXqlU8sorr3Dt2jW2bdumVwtRmBo1amBubs6lS5ekECGEEEIIIf47XpTZo93c3HBzc3tsvvr165OSksKhQ4eoU6cOAAcPHiQlJYUGDRoUudz9AsSlS5fYvn07rq6uj/2us2fPolQq8fb2LvZ2GNwnQgghhBBCCPF0lC9fnjZt2jBo0CAOHDjAgQMHGDRoEB06dNDrVF2uXDlWrlwJQF5eHt27d+fIkSMsWLAAlUpFTEwMMTEx5ObmAnDlyhXGjx/PkSNHuH79Ohs2bKBHjx5Ur16dhg0bFjs+qYkQQgghhBAvPPULUhNREgsWLOC9996jVatWAHTq1InffvtNL09kZCQpKSkA3L59mzVr1gBQrVo1vXzbt2+nadOmWFhY8O+///LLL7+Qnp6Ov78/7du3Z+zYsZiamhY7NilECCGEEEII8RxycXFh/vz5j8yjeaAdV1BQkN77wvj7+7Nz584njk2aMwkhhBBCCCFKRGoihBBCCCHEC0/z/2zG6ued1EQIIYQQQgghSkRqIoQQQgghxAvvRRni9f8LheZxvS+EEEIIIYR4zs198r7CBhkQUTrfW9qeq5qITSdySzsEg7WpZsG1K5dLOwyDlAkJZfvprNIOw2DNKltz4lJcaYdhsGph7ny3XF3aYRhkdDcTflr94j6HGNlZwdqjeaUdhsE61jQjY9qnpR2GQWwHT+REq8alHYbBqm3ZzXrz8MdnfE61V0Zy9vKd0g7DIBVDvbl98Uxph2Ewv7KV2HU2o7TDMFiTiralHUKR/j8O8fo8kz4RQgghhBBCiBKRQoQQQgghhBCiRJ6r5kxCCCGEEEIYQnr5PltSEyGEEEIIIYQoEamJEEIIIYQQLzypiXi2pCZCCCGEEEIIUSJSiBBCCCGEEEKUiDRnEkIIIYQQLzyZJ+LZkpoIIYQQQgghRIlITYQQQgghhHjhScfqZ0tqIoQQQgghhBAlIjURQgghhBDihadWl3YE/y1SEyGEEEIIIYQoESlECCGEEEIIIUpEmjMJIYQQQogXnnSsfrZeyEKERqNh07I/2PfvMrLSUwkMq0z3Nz7F2z+0yGXu3LrMhiW/c/vaORLjouk6YDRN2/cvkC858S5rFvzM+RN7UObm4OEdSO8h4/APrmhQrGvXrWPZ8hUkJiYSGBjAkLfeolKlSkXmP3X6NNNnzODGjZu4urrQo1t32rdvp5dn5apVrFu/gbi4OBwcHGjcqCGvv/YaFhYWAKhUKubNX8D2HTtISkrCxcWZli1a0LtXL0xMnrzySaPRsG7JVPb8s4LMjFSCQivRe9AYfB6x/6NvXWbtoj+4cfUciXF36PHaKF7q0E8vz6YVMzl+8F9ioq5jYWFJcHhVuvZ7Hy/foCeKddnCWfy7eQ3p6WmEla3AG2+PxD8w+JHLHdy7g8Xz/+TunSg8vX3p1X8QdRpE6D4/d+YEa5cv5NqVSJISExj16dfUrt+kwHpu37rOwtl/cO7MCTQaNX4BZRjx0XjcPLwM3p7j//5O5OEl5GSl4u5fhQadPsfZM6xYy185uZ4di0cRUP4lWvb/TZd+59phTu+eRULUWTLT4nip368EVWhhUIyPi//o1t84f1Abv0dAFRp1+QIXr6Ljv3p6C8e3TSM14SZqVR6OboFUafI6ZWt21uVRq/I4svU3Lh9fS2ZaPDYO7oTX7EqNl95GYYRj/sH4tyyfwsFtS8nMSCUgtAovv/4ZXn5FH/sxty+zeemv3L52jqT4aDr1/4gmbQfo5cnOymDz0smcPvIv6SmJ+AaVp/OAjwkIqWy02JecuMLcI5HEZ2QT7OrAqKZVqeHnXmjeI7dieWvprgLpy19rRRkXhwLpmy/cYsyGgzQN8eGnzg2MFvODXDt2waNHb8xdXMm+cZ2oPyaTceZUkfmdm7fEo0cfLH39UGVkkHrkINHTf0eVlqrLY2prh9frg3BqGIGpvR25MXeImvY7aYcPPJVteByXRrUI/mAgjjUqYeXjwZFuQ7m75t9nHsfGdatYvWIRSYkJ+AeU4Y233qVCpSpF5j97+gSzZ0zh1s1ruLi40aV7L1q3y/993rxxjUXzZ3PlciRxsXd5fdA7dOzSQ38dZ06yevkirly+SFJiAh99NoG69RsbZXtWr9/EkhWrSUhKIijAn6GDXqdKxQqF5k1ITGLqzDlcvHKVqOg7dO3YjncGvaGXZ/3mrWzZtpPrN24CUDY0mIED+lKubPHOw09Ko9GwdvE0dm1dQWZGGmXCKtFn0Mf4BoQUuUzUzSusWfQHN66cJyHuDj1f/4AWHfs+k3jF/18lvrqqVCp27txJUlLS04inWP5dM4vt6+fS/fVPGPn139g7ujFl4ltkZ2UUuUxuTjZunn507P0+Dk5uhebJTE/hly8GYGpqxpAxfzDmx1V07j8Ka5uCF83i2LlzF9Omz6BXz578/utkKlWsxGdfjCU2NrbQ/DExMXz+xVgqVazE779OpucrPflj2jT27Nmry7Nt+3ZmzZ5Dvz59mD5tKiPeH87OXbuZPWeOLs+SpUvZsHEjQ98ewvRpUxn4xhssW76CNWvWGrQdD9uyag7/rptPr4Ef8/G3C3B0cuOX8W8XY//70rXv8CL3/8VzR4lo05OPvpnL8C+molapmDzhbXKyswyOdc3yBaxftZjXh4zk65/+xNHZlYmfjyArM7PIZS6eP8Ok/42lcbPWfPfrHBo3a82k/33Bpcizujw52VkEBofy+pCRRa4n5k4UY0cPxccvkLHf/Mp3v86hW6/XMLewNHh7Tu36kzN751C/42d0GroEazs3Ns0aSG5O0fv+vrSkKA5t/B7PoJoFPsvLzcLFK5z6HT8zOLbiOLnjT07tnkPDLp/z8ntLsbF3Z/2MN8jNTi9yGSsbR2q8NIQu7yyi+8jVhNd+mR1LP+FW5G5dnhM7/uT8gUU07PI5PUetp167UZzcOZMz++YbNf7ta2eya+NfdH3tU4Z/tRgHRzemf/3mY479LFw8/GnXawT2RRz7S2d8wcXT++n99reM+t9KylZuwPSv3yQl8a5R4t4ceYsfdpxgYN3yLOzXguq+bgxbuYc7qUX/DgBWvt6aLYM76F4BTvYF8kSnZvDzrlNU9y1824zBKaI5vkPe4+7CeUS+PZCM0ycJnvg95u4ehea3rViZgA8/JWHzOi68NYDrX32OTXg5/Ed+pMujMDMj5NufsPD05vqEzzn/Rl9u/fwdyoS4p7Ydj2Nqa0PqqUjODh9fajHs2bWN2TN+o1vPfvw4+U/KV6rMV2NHExdb+LF4N+YOX439mPKVKvPj5D95uWdfZk77lf17d+ry5OTk4OnlTf/X3sLJ2aXQ9eRkZxNUJoRBQ4YbdXu2797LlD9n0+eVbkz75QcqVyzPmC8ncje28P+zUqnE0dGBvq90I6RMUKF5Tp4+S/Mmjfjx63H8+v3XeLi7M/qL8cQlJBg19qJsWvkXW9cuoM+gj/j0f/NwdHLl53HFuwa/3P89HIs4D/1/oNGUzuu/qsSFCFNTU1q3bk1ycvJTCOfxNBoNOzfMp1XXQVSt2wKfgDD6vTMRZU42R/esL3K5wNBKdO73ATUatsXM3KLQPP+smYWTqxd9h35FYGhlXD18Ca9cDzcvf4NiXbFyJa1btaJtm9YEBAQwZPBbuLu7sW79hkLzr9+wAQ8Pd4YMfouAgADatmlNq5YtWbZihS7P+fMXqFihAs2aNcXL05OaNWrQNCKCi5cu6+WpV68udevUwcvTk8aNGlGjenUuXrpk0HY8SKPR8O/6BbR9+U2q13sJ34BQXh02gdycLA7t3ljkckGhleg2YCS1G7XBzNy80DzvfTaFBs064+Mfil9QOAPeGUdi/B1uXj1ncKwbVi+la88B1G0QQUBQMO+M/JScnBz27NxS5HIb1iyhSvVadH2lP77+gXR9pT+VqtZkw+olujzVa9WnV/+3qPtA7cTDFs2dTvVa9en3xlDKhJTF08uXGrUb4OjkbPD2nN03l6pNBxNUqRUuXmWJ6PEtecpsrp5Y98hl1WoVO5eMpkaLd3FwKXg8+4c3oVar9wmq1Mqg2IpDo9Fwes9cajQfQnBlbfzNemrjv/yI+H1C6lKmUkucPUNwdA2gcqMBuHqFE3P9mC7P3RvHCaz4EoHlm2Lv4kdwlTb4lW1I3O0zRo1/96Z5vNT5LSrXaYm3fxi93v6a3Nxsju8r+twTEFKZjn1HUb1BO8zMCp57lLnZnD60lfZ9PiCkfC3cvAJp3f0dXDx82ffPIqPEvuDoRbpUKkPXymUIdnXgw2bV8LS3YdnJK49czsXaEjdbK93L1ESh97lKreGzDYcYUr8Cfo62Rom1MO7depK4aT2Jm9aRc+sGUVN/RRkXi1vHroXmtylfkdy7McSvWk5uzB0yzp4mYf0abMqG529b6/aY2jtw7csxZJw7jTL2LhlnT5N99dH75GmK27yLi2MnEbNqa6nFsHblUl5q1Y6WrTvgFxDIwLeG4ermweYNqwvNv3nDGtzcPRj41jD8AgJp2boDzVu2ZfWKxbo8YWXL8erAt2kU8RLmRZz/a9SqS58Bb1KvYcEa3SexbNVa2rZsTvvWLQj09+OdQW/g4ebK2o2bC83v5enBu28NpFXzptja2BSa55NR79O5fRtCg8sQ4O/HyHeHoFFrOH7ytFFjL4xGo+HfdQtp120gNeq9hG9gKK+/N57cnGwO7ir6GlwmrCI9Xh1BnUati7wGC1FSBtXzV65cmatXrxo7lmJJiL1NanI85arkV5mbmVsQUqEm1y6efKJ1nzmyA//gCsz+aSSfDorgu496sO/fZQatS6lUcunyZWrUqK6XXqN6Dc6fP1/oMufPX6BG9Rp6aTVr1uDSpUvk5eUBULFiBS5dvkxkZCQAd+7c4fCRw9SpXUu3TMWKFThx4iS3b0cBcPXqVc6eO0ftB/IYKj42itTkeMpXra9LMze3IKxCLa5Gnnji9T8oK1P7dNrGztGg5WPvRpOclECV6nV0aebmFlSoVI2L54u+ubx44YzeMgBVa9R95DIPU6vVHD+yD28ffyZ+PpJBfTvw6chBHN5fsIlIcaUl3SYrLR7fsIa6NFMzC7zK1ObuzeOPXPbEtilY2ToTXqu7wd//pNISb5OZFodfWf34vYNrc/fGo+O/T6PRcPvSfpLjruFdJv949ipTk6jL2nSAhOgLxFw/RkC48W5IEmNvk5YcT3iV/PjNzC0IKV+L6xeLF39hVCoVarUKc3P9GipzcyuuRRq+3vuUKjXn7yZTL9BTL71+oCcnox/95LT3/H9oNW0dg5fu5PDNgjWo0w+cw9naki6VyzxxnEVRmJlhE1aWtGOH9NLTjh7GtkLhTUMzzp3B3M0d+9r1ADBzcsapcVNSD+7X5XGs35CM82fxGzaSiotXEz79Lzx69QcjNn970SiVSq5cjqRq9dp66dVq1ObC+bOFLnPxwlmq1Xg4fx2uXIrUXbdKi1Kp5OLlK9SqXk0vvWb1qpw9H2m078nJySVPpcLezs5o6yxK/N0oUpLjqVitni7N3NyCshVrciWy6OZ9QjwNBvWJmDhxIqNGjWLChAnUrFkTW1v9J1AODoY1/ymOtGTtRc/e0VUv3d7RlaS4O0+07oTY2+zduoSm7QfQsusgblw+zYrZ32JmZkGdiE4lWldqaipqtRpnJye9dGdnJxKLaAqWlJSEs/ND+Z2cUKlUpKSm4uriQtOICFJSUvjgw9FoNBpUKhUd2rej5yuv6JZ5pUcPMjIyGTR4MCYmJqjVal4dMIBmTZuWaBsK3a6keAAcnPSrpB2cXEh8wv3/II1Gw7K/fiS0XHV8A4pub/4oyUmJADg+FKujk3ORVfP3l3u4tsDRyVm3vuJITUkiOyuL1cvm07P/IPq+/jYnjh7gx68/5YuvJ1OhcvXHr+QhWWnafW9tp18VbW3nSnpydJHL3b1xjMgjy+k6bGWJv9OYMtO0zQes7fR/u4+LHyAnK435EyNQ5+WiMDGhUdexeoWRak0HkZudxuIf2mGiMEWtUVGn9fuEVu9gtPjTUrT73+6hc4+dgytJ8Y+O/1GsrG0JDKvG1pVT8fANxt7RleP7NnDzyincvAKfKGaA5KwcVBoNrrb6hRQXG0sSMrMLXcbN1prPWtSgvKczuSo1G87fYMiyXUx/JYKa9/pRnIiKZ/WZ6/zd3/h9Zx5k6uCIwtQM5UPnTWVSEvZFNI3JPHeGG/+bQNCn4zCxsEBhZkbKvt3c/n2SLo+Ftw921bxI2raVq599iKWvP37vjkBhasrdBXOe4hY9v9JSU1Cr1TiV4PyXlJRItYfyOzk5o1KpSE1NwcXFtdDlnoWU1LR712H9B1HOTk4kGrE1xYy/5uPm6kLNakX3GzGWlHv3QA5O+vvVwcmFBCNeg19U6v9w06LSYFAhok2bNgB06tQJhSK/eluj0aBQKFCpVI9cPicnh5ycHL00S0tLQFEg75Hd61g8I7996OCPf9f+oXgor6aQtBLSqNX4h1SkY29tm0y/MuWJuX2FvVsXl7gQofNQTPf30SMWeCi/furJU6dYtHgx7wwdSrnwcKLvRDN12nScnf+mb5/eAOzctYtt27fz0egPCQwI5MrVq0ybPh1XVxdatijZBf/grvUsnP6V7v07Y369t1kFt+tJ9/+DFv35DbdvXOTDr+YUe5nd27cw4/fvde8/HvsdUMihUoxQC25fwbRHUd87k9Wq14j2XXoCEBQcxsXzZ9i6cVWxChGXT6xl76ovde9bDfhDG9tD+TQaTSGpWrk5GexYMppGXcdjZWtYMypDXTq2ll0rxuret319qvaPQvfjo/ethaUt3d9fiTI3k6hL+9m/9lscXPzwCakLwJWTG7h0bC0v9f4BZ89QEqIvsG/t19g4eBBeq/AmL49zbM86ls38Uvd+4Oj7+7/Af6BEx0Zheg/9hiXTPmfCO80wMTHFN6g81Ru05/Z1w5ryFe6hY5rCtkUryMWeIJf8/g9VfVyJScti3pGL1PRzJyNXyWcbD/F5yxo4Wxvex6dEHmp4rN3lhd8xWAYE4Td0ODEL5pB25BDmLq74DBqK//BR3Prpf/dWYEJecjK3Jn0PajVZly5i7uqGR/fe/9lCxH0FjmeN5pHnzALny3v/F+NdEZ5QYdtjpFUvWr6K7bv28OPX43SDmxjTgZ0bmD9tou79sE8nF55RU/TvWYinxaBCxPbt25/oS7/55hvGjRunlzZ27FjqdfmkQN5KtZoRGJZfus9T5gKQlhyPo3P+yCJpqQkFaidKysHZHS9f/dENPH2DOXnwn5Kvy8EBExOTAh3Qk5NTCtRO3Ofs7Fwwf0oypqamutqdufPm07x5c9q2aQ1AmTJBZGdnM/nX3+jdqycmJib8OXMWr/ToQdOICF2e2NhYFi9ZWuJCRNXaTSkTlj9CTF6edv+nJCXo7/+UJBwcC38qWFKLZn7LqSM7+WD8LJxdPR+/wD216jYiLDx/xA3lvWMlOSkRZ5f8p/epKUkFaice5OTsUuCpm3aZ4t+EOzg4Ympqiq9/kF66r38gF84Vr91sQPnmePjnH/uqe/s+Mz0eG4f8DqXZGYkFnu7fl5Zwk/SkKLbOG6pL02i0U3rO+qwS3UdswME1oFjxlFRghWZ0DygYf1ZaPLYPxJ+VnoCN/aN/uwoTExzdtE/l3XzKkxx7lePbp+sKEQfWf0+1ZoMIrdYeAFfvcNKTozmxfbrBhYgKNZsxMvTBY18JaGskHB449tNTEwvUTpSUm2cAQ7/4i5zsTHKyMnBwdmfe5A9wcfd7ovUCOFlbYqpQkJChX+uQlJmDi03xCwCVvV3YcF47Gs3t5AyiUzN5f9U+3efqezf5tX9ezorXW+PvZJymHarUFDSqPMxd9H+zZk7O5BVRq+vZqx8ZZ08Tt/RvALKvXeF2dhZhP0/hzpw/yUtMIC8xAU1ent4Ut9k3r2Pu6orCzEz72X+MvYPjveuW/vkvJSW5yHOmcyHny5Rk7XXL3sGwpqjG4uhgf297kvXSk1KKvg6XxJIVq1m4dDnfTxhbZCfsJ1WtTgTBZfOb7SmV2vNQanICTi7556HUlMQCtRP/RZpS6+X83yzAGVSIiIiIYPfu3UybNo0rV66wbNkyfH19mTdvHmXKPL5t7JgxYxg5Un9UG0tLS7YX0lXAytoWK+v85lIajQYHJzciT+3Hr0x5QHtxv3LuKB37vG/I5uiUCa9G7J3remmxd67j7O5d4nWZm5sTFhrK8ePHadggv//G8ePHqVevXqHLlC9fjoMH9dv9Hjt2nLCwMMzMtP+qnJxsTB56qmJiYoJGo9H9eHJycjAxKSSPAfPBF7X/z5/aT0BwOQDylEounTtC137vl3j9D9JoNCya+S0nDm1j5Lg/cfP0LdHy1jY2WD/QEU6j0eDk7Mqp44cpE1JWF+u5Myfo89qQItdTtlwlTh0/rKtBADh1/BBlyxc9NO/DzMzNCQkrz52oW3rpd6Ju4e5RvIKRhaUtFpb6+97a3o3oy/tw89EWllR5ucRcO0zt1h8Uug5H92C6vqffIfLo1skoczKo12EMto6GDTVbrPit7LCwyr+R1Gg02Ni7c/vSPtx88+O/c/UwddsVHn9RNGh0hRKAPGUWCoV+W3aFwkRXYDJEYce+vZMbF0/vwzfo/rknlyvnj9C+d9GjdJWEpZUNllY2ZKanEHlqLx2MsF5zUxPKezpx8OZdmofl/6YO3LhL0xCfYq8nMjYZN1srQFtTsWRAS73Pp+w9S0aukg+bVcPLvvAOqYbQ5OWReeki9jVqk7I3f0Qu+xq1Sdm/p9BlTCwt0TxUI/7w+S/j7Gmcm7XQPqW+d+609PVHmRD/nyxAgPa6FRIazsnjR6jXIH941ZPHj1CnXsNClylbriJHDu3TSzt5/DAhYeG661ZpMTc3p2xoCEePn6RR/bq69KMnTtGwbu1HLPl4i1esYsHi5Xw77nPCwwxrclschZ2HHJ3cOHfygN41+OLZo3Tr/95Ti0OIwhj0C1++fDn9+/enb9++HD9+XNc0KS0tja+//poNGwoffeg+S0vLe82XHpZbSJo+hUJBRLt+bF31J27egbh7BbB11QzMLa2o2ai9Lt/83z7B0cVDV7DIy1MSc/uK7u+UpFhuX7+ApZUN7l7aJ7FN2w1g0hf92bJyBtXrt+bG5dPs/3c5PQd9UYy9UtDLXbvy/Y8/EhYWRvly5di4aROxcXG0b6ed92HW7DkkJCTw4SjtDVT7du1Ys3Yd06bPoG2b1py/cIHNW7bw8ejRunXWrVOXlStXEhISom3OFH2HufPmU69uXUxNTbV56tZh0aLFuLu7ExgYyJUrV1i5ciWtWrUsGGQJKRQKXmrfl00rZuLhHYiHdwCbVvyJhaU1dRq31eWbPfkznFw96NpXe1LLUyq5c2//q/LySE6M5dY17f738Nbu/7///JrDuzfy9keTsLKyJeVe/wtrGzssLK0MirVd5x6sWjoPbx8/vHz8WbV0LpaWljSKyB+F6LcfJ+Di6q4rWLTt1IMvP3qX1cvmU6tuY44c3M3pE0cY990U3TLZWZnE3InSvY+9e4frVy9hZ2evmwOi48u9mfTdWMpXrErFKjU4cfQgRw/tY+w3RVRHF2N7KjYYwMkd03FwDcTBNZCTO6ZjZm5FcLX8tv87l36EjYMntVuPxMzcEhevsnrrsbTWNlN5MF2Zk0Fqwk3d+/TE2yREn8fSxhE7p+LfaD4u/sqNBnB82zQc3QJxdAvk+LZpmJlbEfpA/NsWfYStowd122p/F8e3TcPdrxIOrgGoVEpuXdjJpaOradQ1v6lUYPlmHN82FTsnb1w8Q4mPPs+p3XMIr93NKLHfj79xm/78u3oGbl6BuHkFsm31dCwsrKjeIP/c8/eUMTi6eNCu1whAW9C4qzv2laQkxhJ1/TyWVja6Pg+RJ/egQYO7dxkS7t5k3cIfcPcOonaEYbUoD+tbsyyfbzxEeU9nqni7suL0VWLSMulWVTtfyq+7TxObnsWEttoBBRYcu4SPgw0hrg4oVWo2nL/Jv5ei+L6jdkAFSzNTQt30nzLbW2pHfHk43Rjili8mYPRnZF68QMa5s7i274S5hwfx61YB4P3GYMxd3bj5vbbZR+qBffiPGE16hy6kHTmIuYsbvm8PI+PCOfIStW3K49etwq1zN3zfHk786uVY+vrh2bs/8asMG0zDGExtbbANza8ZtCnjh0PVcuQmppB969m0d+/YtQeTf/ya0LBwwstVZMumtcTH3aVVO22T3vlzppOQEM/wD7QtB1q368TGdSuZPeN3WrbuQOSFs/y7ZQMjRn+uW6dSqeT2zesA5OXlkZgQz7Url7CytsbbR1vblpWVSUz0A+fUmBiuXbmEnb1DsR+8FKZ7l458+9NkyoaFUKFcOOs3bSU2Lp6ObbXXgD//mk98QiIfj8y/Ab98VTtAQ1Z2NikpqVy+eg0zMzOCArQj2y1avoo58//mk1Hv4+XpruvnaG1lhbW1tcGxFodCoeClDn3YsHwWHt4BeHoHsGHFLCwsrajbJP8aPPOXz3F29eDlfsMA7TU4+rZ2QJy8PCVJibHcvBaJlZW17hosREkZVIj46quvmDp1KgMGDGDRovwhCBs0aMD48U9/fOuXOr2BMjeHZTO/IjMjlcDQyrz9yTS90npSwh0UDzyNT0mM5fuP8ie32bZ2DtvWziG0Qi2GjZ0NaIeBHfjBJNb9PYnNy6fi6u5L11dHU6uxYZ0zIyKakJqWyoKFf5OUmEhgUCATxo3D01PblCMxKZHYuPyxqr28vJgwfhzTps9g3bp1uLi68vbgwTRqlP8EqE/vXigUCv6aO4+EhAQcHR2pW6cOr72aP3nV0CFDmDtvPr//PoXklBRcXVxo27atrs/Ek2rV5TVyc7P5e8bXZGakUiasMu99/ofe/k+M19//yUmxTPywl+791jVz2bpmLmEVavLB+JkA7Nq8FICfxr6p930D3hlHg2adMUSnbn3Jzclh5h8/kZGeRmh4BT4Z/7NejUVC3F29SfjCy1dm+OgvWTx/Bovn/4mnly/DPxpPWHj+hINXLl1g/Cf5F525f2r7ikS81JahIz4FoE6DCAYNHcWqpfOZPX0SPr4BjPzkK8pVrGrQtgBUafImKmUO+9aMJzcrFXe/KrR+/U+9Gov05DsFnso/TnzUWTb8+aru/cEN2nbjYTW60KT7NwbH+7CqTd8kT5nNnpXjyclKwcO/Cu0HzdSrsUhPjtZrY63MzWL3yvFkpMRgZm6Fk0cZmvX6jtBq+ZMwNuz8GYe3TGbPyvFkpSdg6+BB+bo9qdliKMbUrONAlLk5rJg9gayMVAJCqjBozIxHnntSk+L4+ZP8UbF2rp/NzvWzCS5fm6GfzwEgKyudjYsmkZwYg42dI5Vrt6Rtz+GYmhlnKMbW4f6kZOUy48B54jOyCXF1YHLXRvg4aOOOz8gmJi1/zgilSs3PO08Rl56FpZkpwW4OTO7SkEbBJa+VNYbkndswdXDAq+9rmLm4kn3jGlc/G43y3gAJ5i6uWDxwo5m4dSMmNja4dXoZ37feQZWRTtqJY9z58w9dHmVcLFfGjMR3yDDCp81GGR9P3MplxC5Z8My37z7HmpWo/+883fsKP2hv1G/NXcGpgWOeSQyNmjQnLTWVJX//RVJiIgGBZfh03P/wuPdwJCkxgfi4/IEpPL28+Wzct8ya8Tsb163CxdWVgYOHUb9h/vDXSYnxfPDeIN371SsWs3rFYipWrsqEb38B4MqlSL4YM0KXZ/af2v6PzV5qzbCRhm97s8YNSU1NY96ipSQmJhEUGMA3Yz/B00N7HU5ITCI2Ll5vmcHDR+n+vnj5Cv/u3I2nhzsLZ2r7da3ZsAllXh7jvv1Bb7kBvV/h1T49edradH0VZW42C6d/S0ZGKsFhlRjxxZSHrsExehNtJifFMeGD/HuALavnsWX1PMpWrMmHE2Y89Ziflf/ynA2lQaExoAGZjY0N586dIygoCHt7e06ePElwcDBXr16lQoUKZGcXPuLH42w68fiaiOdVm2oWXLty+fEZn0NlQkLZftrwCd1KW7PK1py4VHoTRD2pamHufLfc8GY3pWl0NxN+Wv3inrVHdlaw9uiL23SlY00zMqZ9WtphGMR28EROtDLOjMSlodqW3aw3D398xudUe2UkZy+/mKP5VAz15vZF480B86z5la3ErrOPnyD0edWk4tObE+ZJ/bq+dK5Hw9pLn4hi8/b25vLlywQFBeml79mzh+DgYGPEJYQQQgghRLEZ0PVTPAGDZtUZPHgww4cP5+DBgygUCqKjo1mwYAGjRo1i6FDjNh8QQgghhBBCPF8MqokYPXo0KSkpNGvWjOzsbJo0aYKlpSWjRo3i3XffNXaMQgghhBBCPJL0iXi2DB5/beLEiXz66aecO3cOtVpNhQoVsHsGU74LIYQQQgghStcTDeJsY2NDrVq1jBWLEEIIIYQQ4gVQujPBCCGEEEIIYQRqac70TBnUsVoIIYQQQgjx3yU1EUIIIYQQ4oUnHaufLamJEEIIIYQQQpSIFCKEEEIIIYQQJSLNmYQQQgghxAtPU2o9qxWl9L2lS2oihBBCCCGEECUiNRFCCCGEEOKFJ0O8PltSEyGEEEIIIYQoEamJEEIIIYQQLzwZ4vXZkpoIIYQQQgghRIlIIUIIIYQQQghRItKcSQghhBBCvPDU0rP6mZKaCCGEEEIIIUSJKDQa6YYihBBCCCFebF8vVpXK937S07RUvre0PVfNme580Ke0QzCY948LyV73R2mHYRCrDm+T+NXg0g7DYC6fTSN9yselHYbB7IZ+S/LxbaUdhkGcqjcn6ZuhpR2GwZzHTCH209dKOwyDeUycw4DP75R2GAaZO8Gb7PVTSzsMg1m1H8LZyy/mvgeoGOrNevPw0g7DIO2VkSzZry7tMAz2Sn0T0qeOKe0wDGY35JvSDkE8J6Q5kxBCCCGEEKJEnquaCCGEEEIIIQwhDfSfLamJEEIIIYQQQpSI1EQIIYQQQogXnlqqIp4pqYkQQgghhBBClIjURAghhBBCiBee5sUdtOuFJDURQgghhBBCiBKRQoQQQgghhBCiRKQ5kxBCCCGEeOFppGP1MyU1EUIIIYQQQogSkZoIIYQQQgjxwlNLx+pnSmoihBBCCCGEECUihQghhBBCCCFEiRhUiLh06ZKx4xBCCCGEEMJgGo2mVF7/VQb1iQgPD8fb25uIiAgiIiJo2rQp4eHhxo5NCCGEEEII8RwyqCbizp07/PDDDzg4OPDzzz9Tvnx5vL296dWrF1OnTjV2jEIIIYQQQjySWlM6r/8qg2oiPD096d27N7179wbg8uXLfPXVVyxYsIClS5cyZMgQowZZHDYNWmDbtAOmDk7kxUSRsnouymuRheZ17DUYm9oRBdKVMbeJ/3700w6VxXtPMmfHUeJTMwjxcmV05whqBPs+drnj16IZOGUpoV6uLPmgny79ckwCUzbt5/ztu0QnpfFh5yb0a1LjqcRuWTMCq/qtMLFzRBUXTeaWJeTdulz0AqZmWDduj0XlupjYOqBOSyZrzwZyT+4rkNWiQi3sXh5EbuQJ0pf+8VTiX3LyKvOOXSI+I5tgVwdGNalMdV+3QvMeuR3H4OV7CqQv69+CMi72uvdpObn8vu8c2y5Hk5ajxMfBhhGNK9OojJfR41+2ZSfz124lITmFMn7ejBjQg+rlwwrNu/3QcVZs3cXF67fJzcsj2M+bQd07UK9qBb08c1Zt4nZMHHkqFf5eHvRp34J2TeoaPXYAyxpNsKzb4t7xc4esf5aSd/tK0QuYmmHVsB0WlWrrjp/sfZvIPbUfAIuqDbGoXBdTNx8AVDE3ydq5GtWdG0aP3bpuc2watcXE3om82CjS1y9EeePiI2O3bd4Zq6r1MbF3RJ2SRMbOtWQf3Q2A08CPsQguV2CxnMiTpMz92ejxA3RtZkfTWjbYWptw5XYuc9elEhWb98hlbKwUdG9hT60KVthYmRCfrGLhxlROXcopkLdDE1teaenA5n0ZLNiY+lS24b7Fe08yZ/uR/PNolwhqBPs9drnj16IY+PtSQr3cWDKq32PzG2LjulWsXrGIpMQE/APK8MZb71KhUpUi8589fYLZM6Zw6+Y1XFzc6NK9F63bddZ9fvPGNRbNn82Vy5HExd7l9UHv0LFLD/11nDnJ6uWLuHL5IkmJCXz02QTq1m/8VLavKC6NahH8wUAca1TCyseDI92GcnfNv880hsJoNBq2r/qdIzuXkJWRil9wFToM+BxP38LPnQBHdizhxL413L2tbb7tE1SBlt1H4Bdc+P9x57rp/LPsZ+q37E+7vp8YLfYlJ68w78gD16yIKlT3K+KadSuOwct2F0hf9mpL3TVrzdkbjNtytECefcM6Y2lmarS4xX+DQYWI9PR09uzZw44dO9i5cycnTpygfPnyDBs2jIiIgjfnT5tVtXo4dB5AyopZKK9dxKb+S7gM+oi47z5EnZxQIH/qqrmkrV+Un2BiivsH35B98uBTj3XT8Ui+W72TT19uTrUyPizbf4qhM1axcnR/vJ0dilwuLSuHz/7eTJ1QfxLTM/U+y85V4ufqSMuqYfyweudTi92iQi1sWr1C5saFKG9dwapGE+x7DyNl6peoU5MKXcbu5UGY2DqQsW4u6sQ4FLb2KEwKVoCZOLpg06I7yptPr7/Nlou3+XHXKT5uVo1qPi4sP32dYav3sbRfC7wdbIpcbsWAFthamOveO1tb6v5WqtQMXbEXZxtLvmtfF087a2LSMvXyG8vW/2vvvsOauvoAjn+TAAESCHsoMhTBgYp7771H7bJW26qdtrWt3W/raK122t066651j6q4t+LeIu4Nyt4j475/RIOBgIBBxJ7P8/A85OTcm99NTk7uuWfcPQeZPHsxHwx7hrqh1Vi+aSfvTPqNhd9/jo+HW4H8R6LO0aROTV57pi9qR0f+3baH9775nZlffkhoUBUAnFUqXuzXnYDK3tgqbNh1+ARf/jkHN42TWWPDGmxrNsSh00Ay1y9Ed/0iyvqtUD/9BinTvkAqpPyo+g1DrnImc+08DElxyBydQJ73Q2cTUB3t6YNkXb+IpNNi36wz6mfeJHXaF0jpKVaLXVmnCeoeg0hbPQftlXM4NG6PZui7JP70CYaURIvbaJ59HblKQ+rymegTbiNXm8eesuAXZIq8KljmqMJt5BfknDhgtbjv1bO1im4tVExbnkJMvI6+7dR8MNSND3+KIzvX8qU0hQI+GOpGaoaBXxYmkZhiwF0jJ8tC/qDKtrRv5MjVWG2ZxH+viCPRfLNiG58+cace3XOC16euYPmHQ+5fjy5YT5Pq/iSmZRaa70Hs2rGFv6b9yojXR1GzZh3WR6ziyzEf8NMfs/H08i6Q/1ZsDF+O+YhO3XoyavSnREWdYNrvP+KscaF5S+PvaU5ODt4+vrRo1ZaZ036z+Lo52dkEBlWjQ6fufPPV52VybPejUDmSejya67OX0XDxr+USgyU7105nz/pZ9B/+FR4+gWxb9Sezvx3G2xPXoXRQWdzm0pkD1Gnag57P1cfGVsmudTOY/e1w3vxqNc6u5p/j9YsnOLhtEd5VrDuse0P0db7fdpyPOoQTXsmdpScu8eaK3Swe0rno36wXOhf6mwWgsrNh2QtdzNIelwaE9F/uFigHpRrO5OrqytChQ9HpdPzvf/8jNjaWw4cP88MPP9C3b9/778DKVG16kLl/G1n7tqG7fZPUlXMxJCegatHJYn4pOwtDWorpz7ZKVWQOKrIOlN0J+F1zdxymf5PaDGgWRlVvNz7o1w4fFzWL9hwvcrsvlmyme/1Q6gX6FnguzN+Hd3u3pnv9UOzKsCKwb9qJnKO7yTm6G0NCLJkbF2FITULZ0HLD0bZqbWwCQkhb+Au6S2cwpCSgv3kZ3fWL5hllMlT9hpG5YzWGpLgyi3/e4fP0rR1I/7BAgtyMV3S81Q4sOXGpyO3cHJV4qOxNfwq5zPTcylNXSMnR8n2vZoRXcsfX2ZH6lT0I8dRYPf6/12ymT/sW9O3QiqDKvrw79Cm83V1ZunGHxfzvDn2K5/t0oVa1QPx9vXj92X5U8fVi5+G8stawdgjtmoQTVNkXPx9PnunRgWD/yhw9U0TvUinZN+lA7rE95B7bgyEhlqxNSzCkJqOs38ZifpuqtbDxr076ot/QXY7GkJKIPuYK+ht55Sdz1SxyDu9Af/s6hsRbZK6bj0wmwzaw4BX+B+HYsitZh3aQfXAH+rgY0tcuwJCSiEPTDhbz21Wvg21gDZLn/ID2wmkMyfHorl9CdzXvfZWyMjCkp5j+7ILDkLS5ZJ/cb9XY7+raXMWqHekcPJ3Njds6pi5Nxs5WRvO6DoVu06aBIypHOT8tSOLcVS0JKXrOXtVyLda890JpJ+O1gS7MXJFCRlbZL9Q+d/th+jcNY0CzOlT1dueD/u3wcXFi0e771KOLN9G9QQ3qBRSsR61l9fLFdOzSg85de+HnH8Cwl9/E3cOL9WtXWsy/fu0qPDy9GPbym/j5B9C5ay86dO7OymX/mPJUD6nB0GGv0aptR2xtLV+gaNCoKYOGDKdZS8vfp4chbv0Ozo75kdgVG8sthvwkSWLvhjm06f0KtRt1wdsvhCdGTEKbk83xyH8L3e7JV7+lacdB+AbUxLNSVfq+OB5JMnDh9F6zfDnZGSyZ8j79XhyPg2PhDdjSmHf4HH3DAulfJ4ggd2dGt6uHt5MjS45fLHI7N4fCf7MAZDKZ2fMeKnurxi38d5SqEdGzZ0/0ej1z585lzpw5LFiwgKioKGvHVjwKBbZ+QeREm/945ESfwDYwpFi7cGzSjtxzJ9EnxZdFhCZanZ6o67dpHhpglt48NIBjl2MK3W7F/lNcT0jh1S7NyjS+IskVKHz90V48bZasvXgaG79qFjexDamLPuYK9s274vLWJDSvjceh4xNgY/4j6NC6F1JGGrlHd5dZ+Fq9gTO3k2nm72WW3izAm+MxBXur7jVowVa6TFvLq0t3ceCaeSNnx8UY6vq48fW2Y3Seupan5m1i5v5o9Fa+GqLV6Thz6SpN65r3DjSpW5MTZ4v+QbnLYDCQmZWNRmX5ypskSRw4cYYrMbcKHSJVanIFCh9/tJfM6wntpShs/Kpa3MS2el30MVexb9YZzcivcH5lDA4dBhQoP+Yb2YFcgZSdYb3YFQpsKgWSe/6kWXLu+ZPY+gdb3MSuZji6G5dwbN0D9w8n4/bOJNTdni4ydoeGrck5sQ+0udaL/Q5PVwUuTgpOns8bgqTTQ/TlXKr7Fx5TgxpKzl/TMqSXhl8+9OKrkR70bqNCZn5OwtBezhw9m82pi9aPPT9jPXqL5iH561F/jl2+Weh2D6Me1Wq1XDgfTb36jc3Swxs05kzUKYvbnD1zivAG+fM34cK5aHS6ooeaCfeXFHed9JR4gsNamtJsbO0IrNGYq+ePFHs/2pxs9HodjirzC0T/zv2CkHptqVa7hdVihju/WbeSaRaQ7zfL34vjNy33ft41aP4WukxZw6tLdhb4zQLIytXRc/o6uk9by9sr9nDmdrI1QxesLCkpieeffx6NRoNGo+H5558nOTm5yG1eeOEFZDKZ2V+zZuZ1X05ODm+++SYeHh6oVCr69OnD9evXSxRbqRoRK1asID4+no0bN9KqVSs2b95Mu3bt8PHx4ZlnninNLktNrnJCplBgyDd0QZ+egsLp/leD5U4uKGvUI3Pf1rIK0SQpIwu9QcJdbd4N6a52JL6QrvUrcUn8tGY3Xz3XDRtF+d3WQ+aoRiZXIGWYj3M2ZKQhV1u++iJ39cSmSjAKr0qkLfmTzA2LsKvZAFW3Z015bPyqoQxvScaauWUaf3JWDnpJwt3RvFvX3UFJQkbBsd0AHip7Pu0Yzjc9m/Jdr6YEuKp5bdkuDt/Ia2xeT81g8/kbGAwSP/dtzrDGNZh35BwzDliej1Pq+FPT0RsMuGmczNLdNU4kJBdv2M78NZvIysmlY3Pz+TLpmVm0GzqKloNH8u43v/HeC0/TtG5Nq8UOeeXHkJFmli5lpCJXWS4/Chd3bKpUQ+5ZifSlU8jctATb0Po4dnm60NdxaNcPQ3oy2ktnrBa73PFuHZOv7KenIldbrmMUrl7YBoRg412ZlPk/k75mAcqwxjj1ed5ifhu/IGx8qpB1sGx6QzVqY92Rkm7eS5CSbkCjLrz30tPVhsa17JHL4fu5iazcnk73lmr6tFWb8jStY09AJVsWb0wrdD/WZKpHnfLVo06qouvRf3fx1XPdy7QeTUtNwWAw4OLiapaucXElOcnyiV9SUiKafPldXFzR6/WkplpvSN5/VXqKsb5WO5vPI1A7u5ueK44Ni7/H2dWbqrXyGgvHI9dw88ppOg981zrB3iPvN8u8l8BdpSQhM9viNh4qez7tVJ9vejXlu97NjL9ZS3Zy+HrecQa5OTG2a0Mm923OV92boFTIeemf7VxNSrf6MZQHSSqfv7I0aNAgjh49SkREBBERERw9epTnn7f8W3Kvbt26ERMTY/pbu3at2fOjRo1i+fLlLFy4kF27dpGenk6vXr3Q6/XFjq1UcyLuqlu3Lnq9Hq1WS05ODhERESxbtuy+2+Xk5JCTY37iplQqC8ldTPk+RJnlXAU4NG6DlJ1J9smDD/b6JZD/Kp6E5Xj1BgMfz4/gta7NCPR0tZCjHFj6shTyBZLJZCBJZKyYgZSTjR7I3LgE9cCXyYj4G+RyVP1eImPNXKQsK145LoKl976wwhLo6kSga95Je11fd26lZzH30Dka3JmMLUkSrg5KPu1YH4VcRk1vV+Iysphz6BwvN7XukBpj/ObBShbSLFm/+wDTl6zh29Gv4qYxP2l3tFcy9+tPyMrO4cDJaH6au4TKXh40rF28nrySyf9FlRVMMz0nN5afVX9BjvFHM2vzUlQDhpO54R/QmY+9VzbtjF2tRqTP/xH0ZXAFN/8vRRHvu+zOcaUumoKUkwVA+tq/cX72DdJWzS0Qu0PDNuhir6G7XvTQuuJqXteeF/vkNXC+n5dU6CFIhb3/gFwGaRkGZq5MQZLg8k0drk4KerRSsXJbOm7Ocgb3cOab2YloH/JF8wLfZUmy+JHoDQY+nreO17o1I9Dr4dSjBb6ThcRWWP67n0lxf8eEPMf2rGbV7LGmx4PfMS7SYam8FPcd3rl2Oif2reWlj2Zja2c8V0lJiGHtgokMHT3dlFYW8kdY1AlroJsTgfcs+lG3kju30rKYe+gsDe5Mxq7j60Yd37w5dPUqu/Pc/C0sPHqBD9rXs2boghVERUURERFBZGQkTZsaFzyZNm0azZs3Jzo6usjbKyiVSnx8LC/wkpKSwowZM5g7dy6dOhmH/s+bN48qVaqwadMmunbtWqz4StWImDx5Mtu2bWPnzp2kpaURHh5O27ZteeWVV2jT5v7jMSdOnMi4cePM0saMGcMrpYjFkJGGpNcjz9frIFdr0Kfd/yqOY5N2ZB7cCSVoeZWWq8oBhVxW4GpZYnpmgatqABk5uZy6doszN24zabmxp8QgSUgSNHj/J/54eQBNq1cp87gBpMx0JIMeWb5eB7nKCUOG5VVYDOkpGNKSkXLyrpro42OQyeTInVyR2dmhcPFA/fQbeRvdqeldP/mdlD8+x2ClIWYuDkoUMhnx+XodErNyCvROFKWOjxtrz1w1PfZQ2WMjl5uNOQ1ycyIhMwet3oCtla56ujirUcjlJCSbv9eJKWkFGgX5bdxzkAlT5vLVqBE0qVOwh0Eul1PFx9hlHhJYhcs3Ypi9MsKqjYi75Ueucubeb5rM0alA78RdxrkCyaYGBIA+IfZO+XExmz+jbNIJ+xZdSf/7Z/RxN6wWN4Ahs5A6RuVUoAfUFGdaMvLUJFMDAkAXdxOZXI5C44Y+4VZeZls7lHWbkrFpudViPnImhwv3XH20tTGWTxcnuVlvhLNKTmp64XMYktP06A3mJy4343S4OClQKCCwsi0atYLxr+Zd5VUoZIQG2NGpqSMvjYu1+lU6Uz2aaqEeVd+nHl2Wrx4d/SN/vDKAptX9rRKbk7MGuVxOUr5eh5SUZDQuBRc/AHB1dSvQS5GSnIxCocDJ2fpzqx53Nep3wK9a3gpKOp1xiF1aSjxOLnlDgzLSElFr3O+7v13rZrJj9VRe+GAmPvdMnL5x+RQZqQn8OXagKc1g0HPl7EH2bV7AmOnHkMtLP0fR9JuVr9chMTOnQO9EUer4urH2zLVCn5fLZNTyduVa8uPRE2F4zCZW7927F41GY2pAADRr1gyNRsOePXuKbERs27YNLy8vXFxcaNu2LRMmTMDLy/gdOHToEFqtli5d8ibYV6pUibCwMPbs2VO2jYj58+fTrl07RowYQZs2bXB2Ltlkoo8//ph33zXv/lMqlSR+8mLJg9Hr0V6/hDKkDjn39CbYhYSRc6rgMmb3sqtWExtPH7L2byv565aCrY2Cmn5eRJ69Ssc6eWOpI89epV3tguPC1UolS/ItQbhoz3H2n7vGd0N7UtntIf7AGPToY65iG1QTbfRRU7JtUE1yzx6zuInu2gXsajYEWyVojSfvCndvJIMBQ1oSSBIpU8wbkw7t+iKzsydzwz8YUiyv2FMatgo5Nbxc2Hf1Nh2CK5nS9129TduqxZ9kGR2XbDYJrZ6vOxHR1zFIEvI7DaArSel4qOyt1oAAsLWxoUaQP/tPRNGuSbgpff+JKNo0Kvzq0frdB5jw51y+eOslWjWoU6zXkiTQWvuyskGPPvYqNkE10d5TXmyDapB71vJkWN31C9jVaGBefty87pSfZFM+ZdNOOLToTto/v6CPvWpxXw9Er0d38zJ2wbXJPX3YlGwXXJucKMtjqrVXz2Ef1hiZnRIp907sHj5IBgP6fKs52Yc1QaawJftowWWPSys7VyI70fzCSHKantrVlFyJMX62CgWEBtqxaEPhw5DOXtXSvK49dzoVAfBxV5CUqkevh9MXcvn4F/Mx1yP6a4iJ1/Hvzowy6eY31qPeRJ69Qse6+evRgvOz1EolS9437/pftPsY+89f47uhvaxaj9ra2lItOJRjRw7SrEXe8qrHjhykSbOWFrcJqVGbg/vNP/tjRw5QrXooNjYPNFjgP0npoDJbcUmSJNQaDy6c2kOlAOOcMp0ul8tnDtDlqfeK3NeutTPYtvpPhr43jcpBYWbPVavVnJFfmk+WXz7jUzx8gmjdc/gDNSDgzm+Wtwv7rtymQ3DeEvD7rt6mbbUS/GbdTi5y4rQkSZyNSybYQzRYH0RhI2wedJRNbGys6cT/Xl5eXsTGxha6Xffu3XnyyScJCAjg0qVLfPbZZ3To0IFDhw6hVCqJjY3Fzs4OV1fz3llvb+8i95tfqWqogwcfbOiPNd7Ye2XsWIvLs6+jvX4R7eVzODTrgMLVg8y9xvWpnXo8jVzjRsrf5vcecGjSjtwr59DFlmwiyYN4vk0DPv17PbX8vKkX6MvSyBPEJKXxZHPjlZOf1uzidkoGEwZ1RS6XUd3XfBynm9oBpa3CLF2r03PhlnFysFZv4HZKBmdu3MZRaYe/h4vVYs/etwlV3xfRxVxBd/0i9g1aI9e4kXPYuDqQQ/t+yJ1cyFg1C4Cck/uxb90Dde+hZO5YjdxRjWPHJ8g5tts0nEMfZz4RUsrOtJhuDYMbBPPZ+oPU8nahrq8by05cJjYtk4F1ggD4Zfcp4tKzGN+1EQALjpzH19mRam7OaA0G1p65xubzN/m2ZxPTPgfWDeKfYxf5bvtxnq5XlavJGfx14CzPhFuebP4gnu3ZkbG/zaJG1QDqhASxYtMubsUnMaCT8WTlt79XEJeYzNg3XgCMDYhxv8/i3aFPEVY9yDR3Qmlnh9rRuCLPrBUR1KwagJ+3B1qdnj1HT7J2ZyQfDnvWYgwPInv/FlS9h6KPuYLuxiWU4S2RO7uSe8S4rrl9277InVzI/Hc2ALmnDuLQsgeqns+TtfNf5I5qHDr0J/f4HlP5UTbtjEObXmSs+gtDSiKyO/MrpNwcU8PDGjJ3r8d54MvoblxGe/U8Do3bIde4k7XfeGVb1WUgcmdX0pZMAyDnWCSqdn1wGjCcjM3LkavUqLs9bbxHRL6hTPaNWpMTdbjMh/St35tB7zZqbiXoiE3Q06etmlytxN7jeb0lLz+hISnVYJrfsGV/Bp2bOTK4hzMbIzPwdrehd1s1GyKN39PsXKnAfSZytBLpmQXTren5tg34dEEEtarcqUf33qlHW9ypR//dxe3UdCYM6lZIPeqI0samQLo19O7/JD9//xXB1UMJrVGbDRGriY+7RZcefQCYN2sqCQnxvP2e8V4CXXv0Yd2/y/lr2m907tqL6DOn2LxhLe988Jlpn1qtlutXLwOg0+lITIjn0oVz2Ds44FvJeG+MrKxMYm/m9cLdjo3l0oVzqJ2cLS4tWxYUKkdUwXm9Oo5BfjjXq0FuYgrZ1wpfPKQsyWQymncZwo7VU3H3DsDdO4Dt/07FVmlP3Wa9TPmWTP0QZ1dvujxpvMC5c+10Ni/7mSdf+Q4Xj8qkJRsby3b2jijtjQ0Vbz/z3lpbOwcc1S4F0ktrcIPqfBZxgFrerua/WXWNFx1/2XWSuPRsxne785t1+M5vlrszWr2BtWeuGn+zeuVdxZ66N4owXzf8XdRk5GpZePQC0XEpfNgh3Cox/1cVNsJm7NixFvOPHTu2QP78DhwwLvdtaciycfhm4cPxnn46b+5gWFgYjRo1IiAggDVr1jBgwIBCt7vffvN7oMscmZmZXL16ldxc8xU56tYt/KY6ZSH7aCSpjmrUnQcYbzYXc52k6d+YVluSO7ugcDHvtpTZO+BQtwkpK+Y81Fi71Q8lJTObqRsjiUvNJNjXnd+G96WSm/HkJz41g9hky8ODCnM7NZ2nf1hgejx72yFmbztEo2qVmfH6k0VsWTK5pw8ic1Dh0Lqn6WZzaQt/Na2TL1drkGvu6bLX5pA2/0dUXZ9BM+wTDFnp5J4+RNY2y0sdlrUuIX4kZ+UybV808ZnZVHN35ue+LUzrbcdnZBOblndCpdUb+HHnSeLSs1DaKKjq7sxPfZqb3UTOx8mR3/q34PsdJ3hm/hY81Q48G16NoY2sP5+gc4tGpKRnMHPpGuKTU6laxZfJH72Br6exbCckpXArPu8q94pNO9HrDXw7cyHfzsy7L0rPNs34/PWhAGTn5PDNzL+JS0hGaWdLQCUfxr3xIp1bNLJ6/NqoQ2Q5qLBv2QO52tm4VOqi3zGk3i0/zsid77kqos0h7e+fcezyFM4vfoSUlUFu1CGydqw2ZVE2aIPMxhb1gJfNXitr5xqyd62xWuw5J/aT7qhG1b4vcicNuls3SJnzg+k+NHInFxT3DI2QcnNI/us7nHo/h9vrYzBkppNz8gDpG5ea7Vfh7o1dYChJM7+1WqyFWbMzAzsbGUN7a3C0l3Pxei7fzE40u0eEu0aBdM/opsRUA9/OTmRQd2e+fMOTpDQ9G/Zm8O/OhzOHqTCmenTDPuJSM4z16Ih+efVoWgaxSQ9nond+rdp0IC01lUV/zyYpMRH/gCA+Hfc1Xl7GeiMpMYH4uLzhbN4+vvxv3CRmTvuNdf+uwM3dnWGvvGm6R4Rxm3jee2uE6fHKZf+wctk/1K5Tjy8m/QTAhXPRfP7xO6Y8f0033k+ifceuvPnux2V6zHdpGobRfHPeIhm1vjM2lK7NWcbxYQ8nBkta9xiOLjeH1XPGk52Ril+1ugwdPd2sxyIlIQa5LK/3eP/mv9HrtCz87W2zfbXv+wYd+o98KHF3CfUjOTuHafvOEJ9x5zerX8t8v1l5w/q0egM/7jhh/pvVr4XZb1ZajpYJmw6TkJmD2s6WUC8N059sQ5iP5eF2FY1U1rOcC1HYCJvCjBw58r4LEQUGBnL8+HFu3bpV4Lm4uDi8vYt/ccDX15eAgADOnTPei8vHx4fc3FySkpLMeiNu375NixbFX2lMJpXiHY+Li+OFF14gIiLC4vMlmdl9r5j3BpVqu0eB7/cLyP63bO6yXNbse71G4pelmZHyaHD73xTSf/+ovMMoNfXrk0g+sqW8wygVl/odSJr4enmHUWquH//O7U9fKO8wSs1rwiyGfFY+V3gf1JwvfMle82d5h1Fq9j1f5dT5ivneA9QO9mWNrXVvjvaw9NRGs2hv2d+PpKw81VxO+p/l16h6UOpXJ5Z3CIX6cGrW/TOVga9fLvx+Ow8iKiqKWrVqsW/fPpo0MY6C2LdvH82aNePMmTNFzom4V0JCApUrV2bq1KkMGTKElJQUPD09mTdvHk899RQAMTEx+Pn5sXbt2mLPiSjVoO1Ro0aRnJxMZGQkDg4OREREMHv2bKpXr86qVatKs0tBEARBEARBKDXJUD5/ZaVmzZp069aNESNGEBkZSWRkJCNGjKBXr15mDYgaNWqwfLlxcY709HRGjx7N3r17uXz5Mtu2baN37954eHjQv39/ADQaDcOGDeO9995j8+bNHDlyhMGDB1OnTh3Tak3FUarhTFu2bGHlypU0btwYuVxOQEAAnTt3xtnZmYkTJ9KzZ8/S7FYQBEEQBEEQhDvmz5/PW2+9ZVpJqU+fPvz6669meaKjo0lJMc57VCgUnDhxgjlz5pCcnIyvry/t27fnn3/+wckpbwngyZMnY2Njw1NPPUVWVhYdO3Zk1qxZKBTFXxSgVI2IjIwM02xxNzc34uLiCAkJoU6dOhw+fPg+WwuCIAiCIAiCdRnKaU5EWXJzc2PevHlF5rl3ZoKDgwPr16+/737t7e355Zdf+OWXX0odW6mGM4WGhhIdbbwjb3h4OFOmTOHGjRv8+eef+PoWf+kxQRAEQRAEQRAqnlL1RIwaNYqYGOOEsjFjxtC1a1fmzZuHnZ0ds2fPtmqAgiAIgiAIgiA8WkrViHjuuedM/9evX5/Lly9z5swZ/P398fCw/rrbgiAIgiAIglCU8lri9b+q2I2I/OvfFuWHH34oVTCCIAiCIAiCIDz6it2IOHLkSLHyleROd4IgCIIgCIJgDQaD6Il4mIrdiNi6dWtZxiEIgiAIgiAIQgVRqtWZBEEQBEEQBEH47yrVxGpBEARBEARBeJSIedUPl+iJEARBEARBEAShRERPhCAIgiAIglDhSWJi9UMleiIEQRAEQRAEQSgR0RMhCIIgCIIgVHgGMSnioRI9EYIgCIIgCIIglIhoRAiCIAiCIAiCUCJiOJMgCIIgCIJQ4YmJ1Q+XTJLEADJBEARBEAShYhv5Q0q5vO6v72rK5XXL2yPVE/HdMkN5h1BqowfI+XFVxWyPjeojY8wcbXmHUWrjhtjyxd+68g6j1D571oZpm8o7itIZ0Qm+X1Exyz3Ae/1kzNtZceMf3FpG9r9/lHcYpWLf6zW2n8os7zBKrW1tR66fPVneYZSaX0gYi/ZWzN/cp5rLWWMbWt5hlFpPbTS/rq249c7IHrLyDqFQoifi4RJzIgRBEARBEARBKBHRiBAEQRAEQRAEoUQeqeFMgiAIgiAIglAaYjTTwyV6IgRBEARBEARBKBHREyEIgiAIgiBUeGJi9cMleiIEQRAEQRAEQSgR0YgQBEEQBEEQBKFExHAmQRAEQRAEocIT909+uERPhCAIgiAIgiAIJSJ6IgRBEARBEIQKzyAmVj9UoidCEARBEARBEIQSET0RgiAIgiAIQoUn5kQ8XKInQhAEQRAEQRCEEhGNCEEQBEEQBEEQSqTYw5lSU1NxdnY2/V+Uu/kEQRAEQRAE4WEQd6x+uIrdiHB1dSUmJgYvLy9cXFyQyWQF8kiShEwmQ6/XWzVIQRAEQRAEQRAeHcVuRGzZsgU3NzcAtm7dWmYBlZYkSRze/Btn9i8iJysVryp1adH3M9y8qxdr+wvH1rBl4WgCanWky/O/lnmsBzf+yul9i8jJTMXbvy6t+3+Om0/hsV48sYHDW6aQEn8Vg16HxiOAem1fJLRhX1Meg17HgY2/cu7wajLT4nF09qRGo/407PgaMrl1R661qyenYXU5DnZwPV5izT49cSnF2zYsUMaTbWyIumpg4TbzBqeTA3RuqKB6ZRk2CkhIhZV7dMQkWi/2NmFyGlSTYW8HNxIg4qCeuKI710xq+8sY0FJB9HUDi3YaLOZpWUtGh3oK9kUb2HDYcp7SkiSJPWt/5fjuf8jJTMUnsB6dnvocj0qFl52zRzewb/2fJMddRa/X4eoZQKOOL1K7aT9Tnn3rp3D26AYSb13ExtaeylXr06bfaNy8q1o9/kObfuXMvjvfU/+6tOxbdNm/dHIDR7ZMITUhr+zXafMiIQ3yyn5uTjoH1//M5VObyEpPwKNSTZr3+RSvKnWsHv+OVb9yeMcisjNTqRxUl27PfY5X5cLjP7xjEcf3riTuxjkAfANq077/O1SuWteU5+cPO5CScLPAto3aD6L7c59bJfZ/dh9j1rZDxKdmUM3HnQ/6tqVB1cr33e7IpZsM+30xwT7uLHpvsCn9fGwCv0fsJer6LW4mpfF+3zYMbtPAKrFaIkkSq/+Zws6NS8nMSCOoehiDRnxMJf9qhW5z8+oFVi78nasXokiIi+GpF0fTqfdzZnnOnjrEhpVzuHLhNClJ8bz24Q/Ub9reqrGvXBPBomUrSUhKItC/Cq+PeJG6tWtZzJuQmMSfM2Zx9sJFbtyMoX/vHrwx4iWzPGvWb2TDlu1cvnIVgJDgqgwb8hw1Qor3e1dSkiSxdcVvHNy+iKyMVPyq1qXXkM/wLqLcH9y2iKN7VnHrurHcVwqsReeB7+B3T7m/1/Z/p7JpyWSad36eHs99UibHURS3Vo2o+t4wNA3CsK/kxcEnXufWqs0PPQ5LJEli//pfObV3EdlZqfj416XtE5/j7lv4+3/++AYObZxCcvxVDAYdLh4B1G/3IjUa59Wb+yJ+Yf/638y2c3TyYNj4XWV2LGVN9EQ8XMVuRLRt29bi/4+KYzumc2LXLNoO/AqNRyBHtv7JuhnDePK9ddgpVUVum5Z0g31rv8UnsOFDifXotukc2zGLDk9PROMZyOFNf7J62ks8+/467OzVFrdROmpo0OFVXL2qIlfYciVqG1sXfYKD2g3/0NYAHNk2ndN7F9LhmUm4egcTd/0kWxd9gp29E3VbD7Fa/K1qy2leU86KPXoSUiXa1FEwpLMNv6zQkasreluNCro0VHD5VsGTa3s7GNbdhsuxEvM26cnIlnB1kpGda7XQaVFTRrMaMlZFGkhIk2hdW85z7RX8vkZ//9gdoVN9OVduF15J+bpB/WpybiWVTUW2f+M0Dm35i27PT8LVK5DIiD9Y/OuLDPs8otCyY++ooVnX13DzqYpCYcuFk1uJmPcJjk7uBNUylp1r5/ZTv81z+ATUwWDQs2v1ZBb/MowXP1uDndLRavEf2z6dEztn0e6piWg8Ajm8+U/WTn+Jp95fh52ykLLvoKF+h1dx8ayKwsZY9rcv/gQHlRtV7pT9HUs+Iyn2HO2f/hpHZy/OHVnFmmkv8tR7a1BpvK0W/56I6URunEWfFyfi7hPIzn//ZP4PL/H6hHUoC3n/r0TvJ6xJT/yq1cfGVsmeiOnMnzyMV8f/i7OrMbZh/1uCZMhrUN++cY75P7xEzYZdrRJ3xJFovlm5nU8HdCA8qBJL9h7n9WkrWP7B8/i6Fj78NC0rh//9vZ4mwVVITM80ey47V4ufu4bO9arz3crtVomzKOuXz2LT6nm88OY4vH0DWLNkGpPHvcoXv67A3sFyHZ+bk42ntx8NW3Rm0czvLebJycnCLzCEFh368Oc3o60e99adu/l9+l+89eoIwmrV4N+IDXw8dgIzf/sRby/PAvm1Wi0ajTPPPfUES1f+a3Gfx06cokObVtSuGYqdrS3/LFvJB5+PZ8ZvP+Lp7m71Y9i5djp71s+i//Cv8PAJZNuqP5n97TDenrgOZSHv/aUzB6jTtAc9nzOW+13rZjD72+G8+dVqU7m/6/rFExzctgjvKqFWj724FCpHUo9Hc332MhouLtsLiSV1eMt0jmybRadBE3H1DOTAxj9Z+edLDP648HMGe0cNjTq/iqu3sd6/dGobmxZ+goOTGwE1WpvyuflUp99rM02P5XJFmR+P8Pgo9eXppKQkvvvuO4YNG8bw4cP5/vvvSUy04uXiEpAkiZO75xDe/hWCwrrg5hNCuycnodNmc+Go5Ur4LoNBz9Z/PqBBp5E4uVV5KLEe3zmHhh1fpWqdLrj7hNDhmUnocrM5d6TwWCtXa0rVOp1x9a6GxsOfuq2H4O4bSuylw6Y8t64cIbB2RwJqtsPZzY9qdbvhV70lcddPWvUYmtWUs/OEgairEreTYfluPbY2UDeo6OIkk8ETrRVsO6YnKa3g863C5KRmSKzYo+dGgkRyBlyKlUhKt17sTULl7Dpl4Mx1ibgUWBlpwNYGwgIKDs/LH3u/Fgq2nzCQnG65gWBrA/2bK1iz30CWFRs+d0mSxOGtc2ja9VVCwrvgWSmE7s9/jS43m6gDhZcd/5CmVA/vjLtPNVw8/WnYfiielUO5ceGQKc/AkTMIaz4Aj0rV8fKrQbfBE0lLusmtq6esGv+JXXOo3+FV0/e0/dPG7+n5Isp+pWpNCQozln1nd3/qtBqCm08osZeNZV+nzebSyQ007TEa36qN0XgE0Kjzmzi7+XE68m+rxr9/0xxa9XyVmg274FU5hL4vTUKbm83JfYXH33/EdzRqPwgf/5p4+Fal19AvkCQDl6L2mvKonNxQazxNf+eOb8PV05+A0CZWiX3ujsP0b1KbAc3CqOrtxgf92uHjombRnuNFbvfFks10rx9KvUDfAs+F+fvwbu/WdK8fip1N2Z54SJLEpn8X0OOJYTRo1pHKAcG8+NYX5OZks2/HukK3C6xem4FD36FJq27Y2tpazFOnQSv6DXqDBs06lknsS1aspnvnDvTs2omAKn68MeIlvDzcWb1uvcX8Pt5ejHx5GF06tEPlaLkB/8noUfTt2Y3gqkH4V/Hj3ZGvIhkkjhw7YfX4JUli74Y5tOn9CrUbdcHbL4QnRkxCm5PN8cjCy/2Tr35L046D8A2oiWelqvR9cTySZODC6b1m+XKyM1gy5X36vTgeB8fym08Zt34HZ8f8SOyKjeUWgyWSJHF0+xwad36V4LpdcPcNofMgY71z9nDh779fcFOq1e2M251zhvC2Q/DwDSXm4mGzfHK5ApWzp+nPQe1W1ockPEZK1YjYvn07gYGB/PzzzyQlJZGYmMjPP/9MUFAQ27eX/RWp/NKSrpOVFo9f9ZamNIWNHb5Bjbl15UiR2x7Z/Dv2KldqNB5Y1mECkJZ4ncy0OPxCzGOtVLUxsfeJ9S5Jkrh+bi/Jty/hW7WRKd0nsCE3zu8lOe4SAPE3zxB7+TD+NdpYLX5XNTg5yjgfk9eToDfAlVsSVbyKPhFvV1dOZjYcPm/5JDzUT87NBImn2ih4/0kbXu1lQ8PqRe+zJFxU4OQg42Js3uvrDXDltoSfZ9Gv06a2nMxsiaMXC+9h6N5IzrmbEpdulU0vRErCdTJS4wis2cqUZmNrh19wY25cKn7ZuXJmL4m3LuEX3LjQfDlZxlaevUrzYEHfIy3xOllpcQW/p1Xv/z29S5IkbpzfS0rcJXyDjGXfYNAhGfQobJVmeRW2SmIvH7K0m1JJjr9OekocVWvnxW9ja0dAaGOuny9e/ADa3CwMeh0Ohby3el0uJyJXEd5qgMW5ZyWl1emJun6b5qEBZunNQwM4djmm0O1W7D/F9YQUXu3S7IFjeFDxt26QmhxPrfDmpjRbWztCajfkYvSxcoysaFqtlrPnL9CofrhZesP69TgVFW2118nJyUWn1+OktnxV+kEkxV0nPSWe4DDzch9YozFXS1Luc7LR63U45iv3/879gpB6balWu4XVYn6cpCYYzxn8Q83rzcrBjYkpQb1/7exekuIuUalaI7PnkuOvMHNMa2Z/0ZGIOe+SEn/NqvE/bAZJKpe//6pS3WzujTfe4Omnn+aPP/5AoTBegdLr9bz++uu88cYbnDxp3Svf95OVFg+Ag9rDLN1B7U5acsFxxnfFXj5M9MGlDHhreZnGd6/MtDgAHNXmXc4OTu6kJxUeKxhP7OZ82RaDLheZXE7r/mOock9jpH77EeRmp/H3tz2QyxQYJD1Nu42iev1eVotf7WA8qcnIMk9PzwKXIn6/qnjKqB8s589/Cx8z5OoEjULl7D1tYMdJPX7ucro3VqDT6zlWxMl78WO/E2u2eXpGtnGYVWH8PCC8moyp6wpfMKC2vwxfVxnT15fdogIZqcayo3IyLzsqZw9SE+9fdv78pA36O2Wn09NjCKzZ0mJeSZLYtmwilas1xLNSiHWCJ6/sO+SL30F9/7Kfm5XGvK/aotflIpfLadlvjKkhbqdU4+0fzuHNv+PiVRUHtQcXjq7h9rXjaNwDitxvSaSnGONXO+d//90tzmcozJalP+Dk4k3VWpZPms4c2Ux2Zhr1WvYvfbD3SMrIQm+QcFebX9V2VzsSn5ZpcZsrcUn8tGY3f418EhtF+a8EnppsrOOdXcyvkjq7uJMQV3hDqLylpKZhMBhwdTE/cXZ1cSExOdlqrzNt9jw83N1oGG55vsGDSE8xvvdqZ/PfV7WzO8klKPcbFn+Ps6t5uT8euYabV07z6ueLrRPsY6iwetNR7U5aMc4Z/hrb1lTvtxs4xqwx4h1Qj86DJuHiGUhmWgIHN/7Bkp+fZdCHq3FQuVr/YITHTqkaERcuXGDp0qWmBgSAQqHg3XffZc6cOffdPicnh5ycHLM0pVIJWO5uzu/8kdXsXDHW9Ljb0D8AyH/NTkJCViDVKDcng62LPqD1gPHYl+GX5ezh1WxfOsb0uOdLfxr/yX+FUYKCR2DOTqniqXeWo83J5Pr5vexZPQlndz8qV2sKwPljazl7eDWdBn2Hm3cw8TfPsHvVVzg6e1GjUelOSOoEyejdLO9znr9FnxfuPWQyKKwxbmcDT7RSsGqvnswcy3nAePQ3EyQ2HzH2csQmGvB0gcahco5dLPnJeViAjJ6N806A/t5+Zx8W4iysiWJnA/2aK/i3iCFKzo7QpaGcBVv16K04j/r0/lVs/Duv7Ax4fYrxn3xlpzh36LRTqhjy8Qq0OZlcid7LtmWT0HhUwT+kaYG8mxeNJ+7GWZ59d8EDxX/uyGp2LsuLv9uLxrJf4DspUfD7kI+tUsUTby9Hm5vJzfN7ifx3Es5uflS6U/bbP/MN2xd/wvwJbZHJFXhUqkVweC/ib5wudfwnIlezZm5e/M++dee7ayH+wuqZ/Pasm87JfWsY8v4cbPL1nNx1dNcSgsNa4+RivbkcYLnKsRS13mDg4/kRvNa1GYGe5XMisW/7WuZN+dL0eOSnP9/5r2DZL+57X64KvPmS1aJeuHQFW3fs4vuvxmFnZ/fA+zu2ZzWrZo81PR78zp3f1wKHUFgJKmjn2umc2LeWlz6aja2dsdynJMSwdsFEho6ebkoTIPrQarYuyqt3eo+wXG8aTxnuf87wzGhjvXnt7F52rjCeM/gFG+vNwJrmoxR8A8OZM6ELZw6soH67Fx/8YMqBmFj9cJWqEdGgQQOioqIIDTWfBBUVFUV4ePh9t584cSLjxo0zSxszZgzqusVbhcS/VgcGVMm74qLXG8/uMtPjcXT2MqVnpyfioLY8ySwt4SrpSTdYP+d1U5okGc8Ap38axlPvrsXZ3b9Y8RQlsFZ7vP3viVV3J9a0eFT3xJqVnlDgSkN+MrkcjYfxyqpH5Zok3b7IkS1TTY2Ivf9+S4P2I6ge3hMAd99Q0pNucmTL1FI3IqKvSdyIz+s9uHtRUu1g7H24S2VvvKJviZsTuDrJGNQhrzFyt+77fLBxQnZSunF/+Vd4ik+BWveZr1CYszckbiTkNT5s7o39nliLit1VDa5qGc+0yWuM3I3906eNE7K9NDLU9jKGd807PrlcRoAXNK6u4KtF+kIbWEUJrtsB38B6psd3y05GajxqTV7ZyUxLwDHfVcL8ZHI5rl7GsuNVpSaJty6wf8PUAo2IzYu+4MLxLTz9zjycXH1KHvQ9Amq1x6uK5bJ/7/c0KyOh0O/pvfGbyn4lY9k/unWqqRHh7O5P71fnoc3NRJudjqOzF5vmv4OTm1+p4w8Jb0/loLz4dXfiT0+Nx8klL/6MtARUzvefzLp3/Qx2rZ3C4PdmFjqBNDnhBpdO7+XJ138pddz5uaocUMhlBXodEtMzcXcqOOY+IyeXU9ducebGbSYtN67EZ5AkJAkavP8Tf7w8gKbVy3b+WL0mbQkKCTM91mm1AKQmJ+DiljcZOS0lsUDvxKNE4+yEXC4nKSnZLD0pJQVXF5cH3v+iZStZsHgp334xhmpBgQ+8P4Aa9TvgV61guU9LyV/uE1Fr7l/ud62byY7VU3nhg5n43FPub1w+RUZqAn+OzRtKbDDouXL2IPs2L2DM9GP/yUm+QbXb4z26kHMGjfk5Q/4RDfnJ5HJcPI31pmflmiTdusihTVNNjYj8bJWOuPuGkBx35UEPQ/iPKFUj4q233uLtt9/m/PnzNGtmHC8bGRnJb7/9xqRJkzh+PG+yXt26BbtXP/74Y959912zNKVSyS9rivf6dkqV2YpLkiTh4OTBjXN78KhkXDZPr8sl5tIBmnR7z+I+NJ5VeeLtlWZpBzf+jDYng+a9PkalebATKFOs9mqz1RMkScLRyZPrZ/fgWTkv1psXD9Csh+VYCyVJpgoGQKfNApn50AOZXG5qHJVGrg4S802CTsuUqOYrJzbRuF+FHAK8ZWw6ZPl14lPgt1Vas7QO4QqUtrDugJ7UO+c2V+MkPPLNq3N3ptCJzMWJPTffpOy0LIkgHxmxd1ZPksshwEvG5qOFxJ4Kf641H4LVvq4cOxtYf9hASiZkZEsF8vRpqiA+VWJPlKFUDQiwXHZUzp5cObMb7yp5Zef6+QO06VuyVWUkSTKdHNx9vHnRF5w/tpGnR83FxePBTxLtlGqzFZeM31NPrp/bg8c9ZT/m4gGadC9F2dcX7BqytXPE1s6RnMwUrp/dRdMepV9tR2mvNltxSZIk1BpPLp3ag69/XvxXog/QcWDR8e+JmMGuNX8waNR0KgUWvuzssV3LUDm7U72u9VbAs7VRUNPPi8izV+lYJ9iUHnn2Ku1qF1zCV61UsmT0YLO0RXuOs//cNb4b2pPKbtabJ1MYeweV2YpLkiTh7OLB6WOR+FetARgbFmdPHWLA82+XeTylZWtrS0hwNQ4dOUar5nknboeOHqdl08LnJBXHP8tWMP+fpUwa9xmh1YPvv0ExKR1UZisuGcu9BxdO7aFSgLHc63S5XD5zgC5PFV3ud62dwbbVfzL0vWlUDgoze65areaM/NL8N3j5jE/x8Amidc/h/8kGBBR+znA1eg+efnn1zo3zB2jZu4T1JubnDPnpdbkk3rpApaoPZ6XKslCcnnnBekrViHj22WcB+OCDDyw+J5PJirzxnFKpvDN8Kb/SnezKZDLCWg7h6LapOHsEoHEP4Oi2qdjY2lMtPG8+wNZFH6Jy9qZJt3exsVXi5mM+3tvO3gmgQLo1yWQy6rYewuEtU9B4BKDxDODw5inY2NmbzV3Y/PeHqDRepobF4S1T8PQLQ+Puj16v5WrUds4eWknrAXndnoE123N4y584ufri6h1M/I0oju2YRY3GT1j1GCKjDLSuIychVSIxTaJ1HQVaHRy/lPf59W+pIC1TYtMRAzoD3E4238fdZVvvTd972sDw7gpah8k5dcVAZQ8ZDavLWRVpvXkG+6MNtKolJzHNQGKaRKtacrQ6OHklr+Lp20xOWhZsOWZAbyjYO3I39rvpuRby5OogK7dg+oOQyWQ0aD+Efeun4OoZiItXAPvWG8tOzcZ5ZWft7A9Qu3jTpq+x7OxbPwVv/zBcPP3R63K5dGoHp/etpNMzY03bbPpnHGcO/ku/V37HTqki4874fzsHJ2zt7K0Wf51WQzi69U7Z9wjgyNYp2NjaE3xP2d/6z4eonL1MDYsjW6fgWTkMZ3d/DHotV89s5+zhlbTun1f2r0XvBEDjGURq/BX2rf0WjWcQoY0GWCX2u/E36TSEXWun4OYdgJt3ALvWTMHWzp6wpnnxr5jxIU4uXnR8whj/nnXT2bbyJ/qP+A4Xj8qmuRV2Skfs7O85WTMYOLZ7OXWb90OuKFXVXKjn2zTg07/XU8vPm3qBviyNPEFMUhpPNjde5PlpzS5up2QwYVBX5HIZ1X3Ne7bc1A4obRVm6Vqdngu3Eoz/6w3cTsngzI3bOCrt8PdwsWr8MpmMTr0GsW7pDLx9/fHy9WfdshnYKe1p2qa7Kd/Mn/6Hi7sXAwa/BRgbGjHXLxr/12lJTrzNtUvRKO0d8PI19jRnZ2USF5s3mTT+9g2uXYrGUe2Mu2fBValKamC/3kz64WdCqlejVo1Q1kRs5HZcPL27dwFg+ux5xCck8tG7b5m2OX/RuDhGVnY2KSmpnL94CRsbGwL9jY37hUtXMGve33wyehQ+3p4kJiUB4GBvj4ODwwPHfC+ZTEbzLkPYsXoq7t4BuHsHsP3fqdgq7anbLK/cL5n6Ic6u3nR50niBcOfa6Wxe9jNPvmIs92nJd8q9vSNKe2NDxdvP/LfW1s4BR7VLgfSHQaFyRBWcN/rAMcgP53o1yE1MIfta+c27kclkhLcdwsFNU3DxDMDFM4CDm4z1TkiDvPd/w/wPUWu8aNHLWO8c3DQFryp55wxXorZz5sBK2j2ZV2/uWvk1QbXbo3atRFZ6Agc2/EFudjo1Gvd72IcpVFCl+qW6dOmSteN4YPXaDEevzWH3yvHkZqXiWaUu3V+abtZjkZEcg0xW/pMEw9sNR6fNZufy8eRkpeDlX5deI2aYXX1IT75ptjKLNjeLncvHk54ci42tPS5eQXR89huCw3uY8rTq9z/2r/+ZHcvGk5WegMrZi1rNnqZRp9expl2nDNjYQK+mCuyVcCNOYu4m83tEaFQgSSUbhnQzQWLhVj2dGihoW09OcprxRnAnLlnvysKeKAkbhUT3RsYb5d1IgPnbzO8R4ewoe2SvZjTpPAKdNodN/4wjOzMF38B6DBw506zspCaZl3Ntbiab/hlnKjtu3lXp8cK31GiYV3aO7TQuhfrPj8+bvV63wRMJa269E/F6bY1lf9eK8eRmpeBVpS49hs8w67HIX/Z1uVnsWjGejJQ7Zd8ziA7PfEO1ennx52ansz/iBzJSYlE6uhAU1pkmXd9BrijePKviatFtOLrcbNbNH09WRgqVq9bluXdnmPVYpCaYx39w2wL0Oi1L/jC/Yt6m9xu07fum6fHFqD2kJN4kvJX13u+7utUPJSUzm6kbI4lLzSTY153fhvelkpux6y8+NYPY5GLecfGO26npPP1D3ryZ2dsOMXvbIRpVq8yM15+0avwAXfu/QG5uDvOnTiQzI5Wg6mGM+vwPsx6LxPhYsxtrJifF8cV7z5geb1g5hw0r5xBSuyGjv5gOwJULp/n+8xGmPIv/Mt5Ponn73rz45vgHjrt965akpqYxd+FiEhOTCAzwZ+KYT/D2Mg5NSUhM4nZcvNk2r7yd14N29vwFNm/fibeXJwtmGMfHr1obgVanY9yk78y2G/LsUwwd9PQDx5xf6x7D0eXmsHrOeLIzUvGrVpeho6eb9VikJMQgv6fe2b/5b/Q6LQt/My/37fu+QYf+I60e44PSNAyj+ea5pse1vjPe8O7anGUcH/ZxeYUFQIMOxnpz2xLjOYN3QF36vprvnCGp4DnDtiXjSb9Tb7p6BdF58DeE1M+rN9NTbrF+7ntkZSTjoHbFJ6AeT436B2e3+9+EUhAAZFIpzpYmTpyIt7c3L71kfhfNmTNnEhcXx4cffliqYL5bZt27+z5MowfI+XHVo3nieT+j+sgYM0d7/4yPqHFDbPni7/vcKe4R9tmzNkzbVN5RlM6ITvD9iopZ7gHe6ydj3s6KG//g1jKy//2jvMMoFfter7H9lOXVoSqCtrUduX724a5EaE1+IWEs2lsxf3Ofai5njW353ZjuQfXURvPr2opb74zs8eguZjD40+KvGGZN8yZUKpfXLW+luiw/ZcoUatSoUSC9du3a/Pnnnxa2EARBEARBEAThcVGq4UyxsbH4+hYcK+rp6UlMzKO7ZrcgCIIgCILweBJLvD5cpeqJqFKlCrt37y6Qvnv3bipV+m926QiCIAiCIAjCf0WpeiKGDx/OqFGj0Gq1dOjQAYDNmzfzwQcf8N57JV1yTBAEQRAEQRCEiqRUjYgPPviAxMREXn/9dXJzjetd2tvb8+GHH/Lxx+W7ioEgCIIgCILw3/Oorqz4uCpVI0Imk/H111/z2WefERUVhYODA9WrVy/k3g+CIAiCIAiCIDxOHuiORmq1msaNH+yum4IgCIIgCILwoCRDxVy2uKIq/zuvCYIgCIIgCIJQoTxQT4QgCIIgCIIgPAoMYonXh0r0RAiCIAiCIAiCUCKiESEIgiAIgiAIQomI4UyCIAiCIAhChSeWeH24RE+EIAiCIAiCIAglInoiBEEQBEEQhApPEhOrHyrREyEIgiAIgiAIQomIRoQgCIIgCIIgCCUihjMJgiAIgiAIFZ4YzvRwiZ4IQRAEQRAEQRBKRPRECIIgCIIgCBWeQTKUdwj/KTJJLKorCIIgCIIgVHAD3jpfLq+77Ofgcnnd8vZI9UTc/vSF8g6h1LwmzOL0+ZvlHUap1AquRMp3b5d3GKWmGf0TmTsWlXcYpebY5iniTu0r7zBKxbN2U1J/GFXeYZSa87s/kvjlK+UdRqm5/W8Kgz66Xt5hlMqCSX6k71td3mGUmrppb3acyijvMEqtTW0V6X9+XN5hlIr61Yn8urbiXv8c2UPGGtvQ8g6j1Hpqo8s7hEKJOREPl5gTIQiCIAiCIAhCiYhGhCAIgiAIgiAIJfJIDWcSBEEQBEEQhNIQw5keLtETIQiCIAiCIAhCiYieCEEQBEEQBKHCEwuOPlyiJ0IQBEEQBEEQhBIRjQhBEARBEARBEEpENCIEQRAEQRCECs9gMJTLX1lKSkri+eefR6PRoNFoeP7550lOTi5yG5lMZvHv22+/NeVp165dgeefeeaZEsUm5kQIgiAIgiAIwiNo0KBBXL9+nYiICABefvllnn/+eVavLvxmnTExMWaP161bx7Bhw3jiiSfM0keMGMH48eNNjx0cHEoUm2hECIIgCIIgCBXe47bEa1RUFBEREURGRtK0aVMApk2bRvPmzYmOjiY01PKdz318fMwer1y5kvbt21O1alWzdEdHxwJ5S0IMZxIEQRAEQRCER8zevXvRaDSmBgRAs2bN0Gg07Nmzp1j7uHXrFmvWrGHYsGEFnps/fz4eHh7Url2b0aNHk5aWVqL4RE+EIAiCIAiCUOFJUtnOTyhMTk4OOTk5ZmlKpRKlUvlA+42NjcXLy6tAupeXF7GxscXax+zZs3FycmLAgAFm6c899xxBQUH4+Phw8uRJPv74Y44dO8bGjRuLHZ/oiRAEQRAEQRCEUpo4caJp4vPdv4kTJxaaf+zYsYVOfr77d/DgQcA4STo/SZIsplsyc+ZMnnvuOezt7c3SR4wYQadOnQgLC+OZZ55hyZIlbNq0icOHDxf7uIvdE7Fq1Sq6d++Ora0tq1atKjJvnz59ih2AIAiCIAiCIFRUH3/8Me+++65ZWlG9ECNHjrzvSkiBgYEcP36cW7duFXguLi4Ob2/v+8a1c+dOoqOj+eeff+6bt0GDBtja2nLu3DkaNGhw3/xQgkZEv379TN0q/fr1KzSfTCZDr9cXd7eCIAiCIAiC8MDKa2J1SYcueXh44OHhcd98zZs3JyUlhf3799OkSRMA9u3bR0pKCi1atLjv9jNmzKBhw4bUq1fvvnlPnTqFVqvF19f3/gdwR7GHMxkMBtO4rKLWyhUNCEEQBEEQBEF4MDVr1qRbt26MGDGCyMhIIiMjGTFiBL169TJbmalGjRosX77cbNvU1FQWL17M8OHDC+z3woULjB8/noMHD3L58mXWrl3Lk08+Sf369WnZsmWx46uQE6sdmnbAsVV35E4u6G7fIH3NArRXzha+gcIGVYe+2NdrjtxJgyEliYztq8k+tBMAl2EfYVe1RoHNcqKPkTJn8gPFuu7fFaxY9g9JiQlU8Q9k2MsjqRVWt9D8J08c5a9pv3Pt6mXc3DzoN/AZuvXIGx62IeJftm3ZwNXLlwCoFhzCc0OHExJa05QnYs1KItau4vYt46SbKgGBPPXsEBo2akpZsAtvhbJxB2QqZwzxsWRtXYb+xsXCN1AoUDbvhl2tRsgcnTGkJ5MTuQHtyX1lEt+9Fm3dx+z1u4hPSadaJS9GP92dBiGBFvMeOXeFn5Zu4HJsHNm5WnzdXXiiTWMGd85r/S/bcZB/9x7l/E1jd2PNgEq82b8zYUF+ZRL/snWb+HvlWhKSUgisUpm3X3qOerUsL/EWn5jMr7MXEH3hMtdjbjGwR2feHja4QL5FqyNYvn4Lt+ITcHFyol3zxrwy+EmUdnZWj9+2XkuUje6UlYRYsrctL7SsKPyCUT01skB6+l9fYUi6bXwgl2PXpDN2tRojU2swJN0me+dq9JfPWD12ZcO22DfvglytQR93k8wNi9BdO1/4BgobHFr3xK5OU+QqZwxpyWTtWkvusYIratjVaoR6wAhyo4+SvvgPq8d+1xOdnOnQRIXKQc75a7n8tSKJG7d1RW7jaC/jqa4aGtd2QOUgJy5Jx/w1KRyNzjblcXWW82x3DfVC7LGzlRETr2Pa0iQu3dBaLfZFm3Yzd+024lPSqFrZm9HP9aV+aFWLebccOMGSLXuIvnoTrVZH1co+vNy/Cy3qmn9XFkTsYMmWvcQmJOHipKJj47qMfLIHSjtbq8VdGEmSWP3PFHZsXEZmRhpB1cMYNOIjKvtXK3SbG1cvsGrhH1y5EEVCXAxPv/genXo/V+axLjp2gbkHzxGfkU1Vd2dGt61LfT/LV1EPXovjlSU7C6QvGdqZIDcnAFadusK4DYcK5NnzZl+UNgrrBo/xvd6//ldO7V1EdlYqPv51afvE57j7Vi90m/PHN3Bo4xSS469iMOhw8QigfrsXqdG4rynPvohf2L/+N7PtHJ08GDZ+l9WP4X7cWjWi6nvD0DQIw76SFwefeJ1bqzY/9DjK2+O2xCsYV1B666236NKlC2CcMvDrr7+a5YmOjiYlJcUsbeHChUiSxLPPPltgn3Z2dmzevJmffvqJ9PR0qlSpQs+ePRkzZgwKRfG/g6VuRGzevJnJkycTFRWFTCajRo0ajBo1ik6dOpV2l8WirNMEdY9BpK2eg/bKORwat0cz9F0Sf/oEQ0qixW00z76OXKUhdflM9Am3kaudQJ73JqUs+AWZIu+tkDmqcBv5BTknDjxQrLt2bGHmtN94+fVR1KgZxoaI1Xwx5kN+/mMWnl4Fx7Ldio3hyzEf07lbT0aN/pQzUSeZ+vuPaDQamrdsC8CpE0dp3aYDNV4Jw9bOjuVL/2bcZ+/z8+9/4e7hCYC7hyfPvzACn0qVAdi6aT2Tvvgf3/88Ff+AoAc6pvxsQ+tj374/WZsWo79xCbt6LVA98Sppf01ESkuyuI1j7xeROTqRGfE3huR45I5qkJf9HP/1B07w7T/r+Pi5XoQH+7N0+0FG/jyXpePexNfdpUB+B6UtT7dvSoifNw5KO46cv8KXc1fhoLTliTaNATgYfYluTepQr1pP7GxtmL1+F69Nns3ScW/i5eps1fg374rk57/m896IodSpWZ2V67cy+svvmPvTRHw8C/6ga3VaXJydGfJEHxb9G2Fxnxu27+HPeYv56I1h1KlRnWs3Y5nwyzQA3nrJuicnNiH1sW/Xn+zNS9DfvIRt3RY49n+F9NkTkdKSC90ufeYEpNy8E1YpK930v7JlT2xrNiR74z/oE29jE1ADxz4vkfH3TxjiblgtdrtajXDs8hSZ6xagvXYB+wZtcHr2TVL+HIsh1XI5Vw8YgVzlTMa/czAkxiFTOSGzUM7lGjccOw1Ee/Wc1eK1pHdbJ7q3UjNlcSIx8Tr6d3Dmk+GevPddLNm5ln94FQr4eJgnqRl6fpqfQGKKHneNgqx78qscZIx9zYvTF3L45q94UjIMeLvZkJFlvVVSNkQe5fv5q/ho6ADCqweydGskb343ncUT38fXw7VA/sPRF2kaFsIbT3bHydGBVTsP8M7kmcwe8xY1Ao314to9h/ll8Vo+H/YU9aoHciU2jrHTjOOG33uub4F9WlvE8tlsXD2fF98ci7dvAGuWTGfyuNf48tfl2DuoLG6Tm5ONh3dlGrbozKKZ35d5jAAboq/z/bbjfNQhnPBK7iw9cYk3V+xm8ZDO+Do7Frrdshc6o7qnMebqYD7UQ2Vnw7IXupillUUDAuDwlukc2TaLToMm4uoZyIGNf7Lyz5cY/PE67OzVFrexd9TQqPOruHpXRaGw5dKpbWxa+AkOTm4E1GhtyufmU51+r800PZbLy+YY7kehciT1eDTXZy+j4eJf77+BUGG4ubkxb968IvNIUsE6/OWXX+bll1+2mL9KlSps3779gWMr1Znbr7/+Srdu3XBycuLtt9/mrbfewtnZmR49ehRoHVmbY8uuZB3aQfbBHejjYkhfuwBDSiIOTTtYzG9XvQ62gTVInvMD2gunMSTHo7t+Cd3VvCuIUlYGhvQU059dcBiSNpfsk/sfKNZVyxfTsUsPOnftSRX/AIa9PBJ3Dy8i1lqemL5+7So8PL0Y9vJIqvgH0LlrTzp07s6KZYtMed55/39079WPoGrB+FXx5/U3RyMZJI4fy5tN37hpCxo2bkblylWoXLkKg4cOx97egbNnTj/Q8Vhi16gduSci0Z6IxJB4i+ytyzGkJWEXbrk7zCawBjZ+1chcNgX91bNIqYnoY6+iv3nZ6rHlN2/jHvq1asCA1o2o6uvF+8/0wMfVmcXbLX/ONfwr0b1pXapV9qaShys9m4XTonYwR85dMeX5asSTPNW+KaH+vgT5evLZkL5IksS+qAtWj3/h6gh6dWxL787tCPSrzNvDBuPl7saK9Vss5vf18mTUsMF0b98KlaPlH/uTZ89Tp0Z1urRpga+XJ03C69CpVTPOXLhk9fiVDduhPbkP7UljWcnZthxDWjJ29VoVuZ0hKx0pM830xz2VpW3NRuTs24TuUhRSSgLa47vRXY7GrlF7q8Zu37QTOUd3k3N0N4aEWDI3LsKQmoSyYVuL+W2r1sYmIIS0hb+gu3QGQ0oC+puX0V3P1+sik6HqN4zMHasxJMVZNeb8urVUs3JrGgdOZXP9lo4/FiViZyujRXjhJ4LtGqlQO8r5YU4CZ6/kEp+sJ/pKLldj8noYerd1IiFZz5QlSVy4riU+Sc+pCzncTrTe0NZ5Edvp27YJ/ds1JaiyN6MH98XbzYUlW/ZazD96cF+G9mxP7ar++Pt4MvLJHvj7eLDj6ClTnhPnL1OveiDdWzSgkqcbzeuE0rVZOKcvXbda3IWRJInN/y6gxxPDaNCsI5UDgnnxrfHk5mSzb8e6QrcLql6bJ4e+Q5NWXbGxLfveEoB5h8/RNyyQ/nWCCHJ3ZnS7eng7ObLkeBG9zYCbgxIPlb3pTyE3X0lGJpOZPe+hsi9kTw9GkiSObp9D486vEly3C+6+IXQeNAltbjZnD/9b6HZ+wU2pVrczbt7V0Hj4E952CB6+ocRcNF+5Ri5XoHL2NP05qN3K5DjuJ279Ds6O+ZHYFcVfnlMQHlSpeiImTpzI5MmTGTkyb6jBW2+9RcuWLZkwYYJZulUpFNhUCiRjxxqz5NzzJ7H1D7a4iV3NcHQ3LuHYugf29Vsg5eaQG3WE9E3LQGe5q92hYWtyTuwDbW6pQ9VqtVw4f5YBTw4ySw9v0IgzUSctbhN95jThDRqZpdVv0JjNG9ai0+mwsSn4ceXm5KDX61A7Wb7qrdfr2bNrO9nZ2YTWrF3KoymEXIHCuwo5+8y7THWXo7GpFESOhU1sguugv3UNu8YdsavVCEmbi+7CSbJ3ry3087AGrU5H1JWbvNittVl6s9rBHLtwrVj7OHP1JscuXOP1fh0LzZOdq0Wn16NRFX5iVhparY6zFy4zuH8vs/TG4XU4eab0V7Dr1gxhw/Y9nD53gVrVq3Ej9jaRh4/RrX3RJ/YlJlcg9/Yj58Ams2TdlTMoKgUWual68GhQ2BgbHvs2oL93CJHCBvT5yo1Oi00ly8NcSkWuQOHrT9Ye894c7cXT2PhZHnpiG1IXfcwV7Jt3RVmnKZI2l9yzx8javsqsnDu07oWUkUbu0d3YVrFch1mDl5sCV2cFx8/l9ejo9BB1KYeQADu27M+wuF3Dmvacu5rDi31daFjLgdQMA3uOZrJqe5qpLdegpgPHz2Xz9iA3alRVkpSqZ+PeDLYesLzPktLqdJy5fIMXeplfKGpWJ4Tj5y4Xax8Gg4GMrByz72V4SBBr9xzm5IWrhFXz5/rtBHYfO0OvVo2K2JN1xN+6QUpyPLXDm5nSbG3tCKndkAvRx2nbdWCZx1AcWr2BM7eSeaFxiFl6M38vjt+03PN/16D5W8jR6anq7sywpjVoXMXT7PmsXB09p6/DIEmEeLrwWota1PBysfYhkJpwncy0OPxD8y5sKWzsqBzcmJhLRwhrUfQKOWBsiFw/F0lS3CVaVHvP7Lnk+CvMHNMahY0d3gH1aN7jHTQeVax+HELxGMrpPhH/VaVqRKSmptKtW7cC6V26dOHDDz+87/aF3ZTjfuSOTsgUCgzpqWbphvRU5GqNxW0Url7YBoQg6bSkzP8ZuaMTTn2GIHNUkbZsZoH8Nn5B2PhUIXV5wedKIi01BYPBgIuLeVe7i4sryUmWhz8kJSVS30J+vV5PamoKbm7uBbaZM2sqbu4e1AtvaJZ+5fJFPnrvDXJzc7F3cOCj/42nin/gAx1TfjIHFTK5AinT/POQMtOQqZwsbiPXuKOoXBVJpyNz5QxkDmocOg1EZu9I1vq/rRrfvZLSM9EbDLg5m3dduzupSUgp+g6NXd//lqT0DPR6A6/0ac+A1oWfZPy8dANeLs40rWXFk1ggJS3NGL+LeTl30ziTkJxSyFb316lVM5JTUnn90y+RJGOjs1/XDjw/oPeDhmzGVFYyzN9rKTMNmaPlBrCUkUrWxoXob11HprDBtmYjHAe+TuaiX03zKPRXzmDXoB366xcwJCeg8K+OTbUwkFlveJzMUX0n9nz1TkYatmrLsctdPbGpEoyk05K25E/kDmocuz+L3EFFxr9zALDxq4YyvCUp076wWqyF0aiNQyxS0sx7B1LT9Hi4Fv4z4OVmQy1XG3YfzeSbWfH4uNvwQl8X5ApYvjnNlKdTUzXrdqWxYlsa1fzsGNrHBZ1eYufhzAeOPTktA73BgLvGvE5xd3a673f3rnnrtpOdk0vnpnkrlHRtVp+k1AyGffkbEhJ6vYGBHZrzYm/LvdrWlJKcAICzi3md7uziRkJcTJm/fnElZ+WglyTcHc17CdxVShKuZFvcxkNlz6ed6lPTywWt3sCaqKu8tmQnU59sQ4M78yiC3JwY27UhwR7OZOTo+PvIeV76ZzsLB3fE39Xy8KLSykwz9vA5OJm/145qd9KSbha5bU5WGn+NbYtel4tMLqfdwDFmjRHvgHp0HjQJF89AMtMSOLjxD5b8/CyDPlyNg6rgMDtBeNyUqhHRp08fli9fzvvvv2+WvnLlSnr3vv/Jx8SJExk3bpxZ2pgxY3i9uAHkH/tVxA03jDfjkEhdNAUpJwuA9LV/4/zsG6Stmlvg6rdDwzboYq+hu26l4Rz5YpOkIsMtcPMQCeOxyii40fIlf7Nr+xa+mDQZu3yTYCtVrsIPv0wnIyOdvbt38PMPk/jy6x+t3pC4E2Tx0sB48JJE5po5cGece9a2FTj2eZGszUvKtDfi7subh3n/G7bM/GA4mTk5nLh4nZ+XbaCKpzvdmxacHD8rYicR+08w7f2XUJbRUIOC8Vu+EU1xHT4ZxZylq3lvxFBqhVTjeswtfpo5j1mLVvDCU/0eKNbiMX4/LTEk3c6bQA3oYy4jc3LBrlEHsu40IrK3LsO+8zOoXvgEkDAkJ6A9tQ/b2mWwiEAJyrnsTjnPWDEDKScbPZC5cQnqgS+TEfE3yOWo+r1Expq5SFnWuWJ/r5bhDgzrn3cS882s+EICLVidmj0tg9QMPdOXJSFJcOmGFldnBT3bOJkaEXIZXLyRyz/rjY2sKze1+Hnb0KmpyiqNiHtCNSMhFUy0IGLvEaYs38APo17EzTmvIXIw6jwzV2/mo6EDCKvmz7Vb8Xw3byUeKzYyol9nq8UNELl9LfOmTDA9fvPTny1nlCzX9eWtwHtfRJkJdHMi0C3vfa5byZ1baVnMPXTW1Iio4+tGHd+8YT/1Krvz3PwtLDx6gQ/a338pyqJEH1rN1kVjTI97j/jzzjHk/22l6B9jwE6p4pnRy9HmZnLt7F52rpiEs7sffsHG+iWwZhuz/L6B4cyZ0IUzB1ZQv92LD3QcQuk8jhOrH2WlakTUrFmTCRMmsG3bNpo3bw5AZGQku3fv5r333uPnn/MqyLfeeqvA9oXdlCNl/CtFvq4hMw1Jr0fuZH41Vq5ywpBu+WqsPi0ZeWqSqQEBoIu7iUwuR6FxQ59wz008bO1Q1m1KxqblFvZUMk7OGuRyOclJ5l2+KSlJaFwsX6FwdXUjKX/+5GQUCgVOzuZXPFcs/Ycli+YzbsL3BAYVHFJha2uL752J1cHVQzl/9gz/rlzKa2++VyBvaUlZGUgGPTKVeWwyRyfj2HVL22SkGj+reybKGhJuIZPJkatdMCSXzbhwV7UjCrmchJR0s/TEtIwCvRP5VfY0fl7V/XxISE1nyuotBRoRc9bvYsbaHfz57guE+PlYN3hA4+RkjD/JvJwnpaTipin9BO7pfy+la9sW9O7cDoBqAVXIzsnhmz/+YsjAPsitNOE9r6yYX02WOaoLLSuW6GOuYFszrydIysoga9UMUNggc1AhpaegbN0bQ0qCVeIGkDLTjbHn63WQq5ww5OuduMuQnoIhLRkpJ6+c6+NjjOXcyRWZnR0KFw/UT7+Rt9GdExrXT34n5Y/PMSQVcuJfDIdOZ3P+Wl7dZqMw7lvjpCA5La+731mtICW98LkLyWkG9HrJ7KTxxm0drs4KFArQ6yEpTV9ghaebt3U0CbPOkD4XJxUKuZz4fL0OianpuDtb7vG8a0PkUcbPWMTXI5+naZj5kJw/lq6nR4sG9G9nPCGsXsWX7JxcvvxrCcP6dLRa2QcIb9KWqiFhpsdarfFiSWpyAi5uecN8UlMSC/ROlCcXByUKmYz4TPNeh8TMnAK9E0Wp4+vG2jOFDxuVy2TU8nblWnJ6oXmKK6h2e7xH59XPep1xWHJmWjwqjZcpPSs9AUd10e+1TC7HxTMAAM/KNUm6dZFDm6aaGhH52SodcfcNITnuisXnBeFxU6pGxIwZM3B1deX06dOcPp03WdfFxYUZM2aYHstkMouNiJLelMNEr0d38zJ2wbXJPZ03uckuuDY5UUcsbqK9eg77sMbI7JRIucYhVAoPHySDAX2+1Zzsw5ogU9iSfbTgEowlZWtrS7XgEI4dOUizFnnj8I8dOUSTZpYnHYfWqMWB/eYTBY8eOUi16qFm8yGWL13IkoXz+PyLbwiubnl5z/wkJNMPl9UY9OhvXcMmMBTd+eOmZJvAULTnT1jcRHfjIrYh4WBrZ5pzInf1RDIYMKQnWze+e9ja2FAzoBKRURfo0KCWKT3y9AXahRdc3rcwEhK5OvOTrtnrdzF9zTZ+e3sote+s/GJttrY2hFQL5MCxk7RtlncSffDYSVo1Kd6dJS3JzslFlm/oj1wuR0Iq8mpjiRn0GG5dx8Y/FN09ZcMmIBTdBctzhCxReFVGyrBwwUCvQ0pPAbkc2+p10UYftULQdxj06GOuYhtU02y/tkE1yT17zOImumsXsKvZEGyVoL1T77h7G8t5WhJIEilTzHtjHdr1RWZnT+aGfzCkWB7yWFzZuRLZCeblNClVT51gJVduGusBhQJqBin5e13hw+HOXsmhRbjj3Q5EAHw9bUhK1XP3lkBnr+Ti62H+U+LjaUN8ctFLxxaXrY0NNQIrs+/kWTo0qmNK33fyLG0bhBW6XcTeI4yf/g8TXh9M6/BaBZ7Pzs1Fnm+yr1wuB0kqtCO1tOwdVGYrLkmShMbFg9PHIvG/s7y4Tqvl7KlDPPF8wd/M8mKrkFPD24V9V27TITivbtt39TZtqxX/hlTRt5OLnDgtSRJn45IJ9rA8LLkk7OzVZisuSZKEo5MnV6P34OlnLAd6XS43zh+gZe+SXlSTTI0SS/S6XBJvXaBS1YaF5hHKlmQQcyIeplI1Ii5dsv7KLcWVuXs9zgNfRnfjMtqr53Fo3A65xp2s/VsBUHUZiNzZlbQlxmUqc45FomrXB6cBw8nYvBy5So2629PGe0TkGzpj36g1OVGHrTa8oE//J/np+4lUqx5KaI3abIz4l/i4W3TtYRzyNXfWNBIT4nj7vU8A6NqjD2v/XcHMab/RuWsvos+cYvOGtbz7wf9M+1y+5G8WzP2Ldz/4FC8vH5ISjQ0hewcHHBwcAJg3exoNGjbFw9OLrKxMdm7fwqkTx/hs/NdWOa575R7chkOPwaYVluzqtkDu5Erusd0AKFv3Qq7WkLVuPgDaqEPYN+uKY7dBZO9Zh8xBjX3bvmhPRpb5UKbBnVvwvxlLqRVQibrVqrBsx0FiE1MY2NZ4F8ifl23gdlIqXw4zTmr8Z+s+fNw0BPoYrxQePX+FuRt280z7vMmQsyJ28vvKzXw1/EkqebiYrpY6Ku1wtC9FQ7kIz/Tuxhc/T6FGcBBhocGs2rCNW/EJ9OtiHMP957xFxCUk8dnbeT165y4Zr4hlZWeTnJrGuUtXsLGxIaiK8YSgZaNw/lkdQUjVAOPE6phbTP97Ka0a1UehsO6yuzmHtuHQ/Tn0t66hj7mMbZ3m5mWlVS9kag3ZEcayYle/LYbURAwJMXBnToRtSDiZq/LmKyl8ApCpNejjbiBXa1A27wbIyDloecWq0sretwlV3xfRxVxBd/0i9g1aI9e4kXN4BwAO7fshd3IhY9Us47Ge3I996x6oew8lc8dq5I5qHDs+Qc6x3aZyro8zH48tZWdaTLeWiN3p9G3vTGyCjth4HX3bO5OrldhzNG/I0WtPuZKYojcNTdoYmUGXFmqG9HZh/Z50fNxt6NvOiYg9eVeM1+1KY+xrXvRt50TkiUyq+dnRoYmKGcserCF0r8Hd2vLZlL+pFVSFusEBLNsWSWxCMgM7GL+LvyxaS1xSCuNfMa6HHrH3CJ9P/ZvRz/WlTjV/4pONx6O0s8XJ0VhPtgmvxfyIHYQGVL4znCmBP5ZG0KZ+bRRlvOS0TCajY69BrF06Ey9ff7x9/Vm7bCZ2Snuatuluyjfjp89wdfdiwOA3AWND4+adFb50Oi1Jibe5eikae3sHvHz9yyTWwQ2q81nEAWp5u1LX141lJy4Tm5bJwLrGeV+/7DpJXHo247sZL24sOHweX2dHqrk7o9UbWHvmKpvP3+TbXnlX76fujSLM1w1/FzUZuVoWHr1AdFwKH3YIt3r8MpmM8LZDOLhpCi6eAbh4BnBw0xRs7ewJaZC3UMWG+R+i1njRopexYXFw0xS8qoShcfdHr9dyJWo7Zw6spN2TeUOldq38mqDa7VG7ViIrPYEDG/4gNzudGo37Wf047kehckQVnFcGHIP8cK5Xg9zEFLKvPTrzbITHS6kaEfmHIt0lk8mwt7enevXq9OnTBzc36y91lnNiP+mOalTt+yJ30qC7dYOUOT9guDNRTe7kgkKT10Up5eaQ/Nd3OPV+DrfXx2DITCfn5AHSNy4126/C3Ru7wFCSZn5rtVhbtelAWmoqi/6eQ1JiIv4Bgfxv3CS8vIzDXZISE4iLyxvz7e3jy//GTeSvab+z7t+VuLm7M+yVN033iABYt2YlOp2Wb74aa/ZaTw8ayjPPvQBAclISP37/FUmJiTiqVAQGVuWz8V8TXt/6q45oo48gc1Bh37wrMpUGQ3wMGcumIN1ZO1+uckbufM/wLW0uGUt+x77DQNSDRyNlZaA9e5TsXWsKeQXr6dq4DinpmUz913jDquBK3vzy1vNUunOPiPjkdGIT867KGgwSvyzbyI34JGwUcvw83XhzQBcGtsl7Hxdt249Wp+f9PxeavdYrvdvzah/rTtDs2KoZKWnpzFq0koSkZIL8/fj20/fw8TKOM05ISuZWvPkwnhff+8z0f/SFy2zcuRcfTw+WTPkBgKFP9kUmkzFtwRLiEpNwcXaiZaP6vPyc9VeH0Z09QraDI8pmXe/cbC6GzOVTTPcTkamckTvdU1YUCuzb9kGm1oBOiz4+lszlU9BdisrLY2ODsmUP5Bp3JG0OuktRZK2bB/cMX7SG3NMHkTmocGjd03SzubSFv5ruTSNXa5Br7qnvtDmkzf8RVddn0Az7BENWOrmnD5G1baVV4yqJ1dvTsLOV8WJfV1QOci5cy2XijDize0S4u9hw75DixBQ9k2bEM7iXhklve5OUqididzqrtucNLbp4XcvkuQk83U1D/47OxCXpmLs6hd1HrfcZdGkWTnJ6BtNWbiQ+OZVqfj78/N4wfD2M73l8ciqxCXmNlmVb96LXG/h6znK+npM3PLVXq0aMe9m4Gs+wvp2QyWT8viSCuKQUXJzUtKlfizcGdudh6NZ/KNrcbBZMnURGRipVq4fxzue/m/VYJMbHmt1bJDkpji/ey7tx1IaVc9mwci4htRvy/hfTyiTOLqF+JGfnMG3fGeIzsqnm7szP/Vqa7hERn5FNbFpeQ1SrN/DjjhPEpWehtFFQ1d2Zn/q1oFVQ3jDPtBwtEzYdJiEzB7WdLaFeGqY/2YYwn7JZHrVBh+HotNlsWzKenKwUvAPq0vfVGWY9FulJN83ml2lzs9i2ZDzpKbHY2Nrj6hVE58HfEFK/R942KbdYP/c9sjKScVC74hNQj6dG/YOzW9n0SBdF0zCM5pvnmh7X+s54cfLanGUcH/bxQ49H+G+QSZbuUHEf7du35/Dhw+j1ekJDQ5EkiXPnzqFQKKhRowbR0dHIZDJ27txJ7drFX1b09qcvlDSUR4bXhFmcPl82VxDLWq3gSqR893Z5h1FqmtE/kblj0f0zPqIc2zxF3Kmyv1t3WfCs3ZTUH0aVdxil5vzujyR+WfRcrEeZ2/+mMOijsr+vQVlYMMmP9H2ryzuMUlM37c2OU9afFP+wtKmtIv3PinlyqX51Ir+urbgTaEf2kLHGtnhDkR9FPbXR5R1CoTo9e7BcXnfT32W/NPSjqFR9tn379qVTp07cvHmTQ4cOcfjwYW7cuEHnzp159tlnuXHjBm3atCm0x0IQBEEQBEEQhIqrVMOZvv32WzZu3IjzPSsGOTs7M3bsWLp06cLbb7/N559/TpcuXYrYiyAIgiAIgiBYhyRuNvdQlaonIiUlhdu3bxdIj4uLIzXVOIHNxcWF3NzS3/FZEARBEARBEIRHU6mHM7300kssX76c69evc+PGDZYvX86wYcPo168fAPv37yckJKToHQmCIAiCIAiCUOGUajjTlClTeOedd3jmmWfQ6YxrgdvY2DB06FAmT54MQI0aNZg+fbr1IhUEQRAEQRCEQhjEHasfqlI1ItRqNdOmTWPy5MlcvHgRSZKoVq0aanXecmnh4eHWilEQBEEQBEEQhEdIqRoRd6nVaurWrXv/jIIgCIIgCIJQhsQdqx+usr0tpyAIgiAIgiAIjx3RiBAEQRAEQRAEoUQeaDiTIAiCIAiCIDwKJDGx+qESPRGCIAiCIAiCIJSI6IkQBEEQBEEQKjxxx+qHS/RECIIgCIIgCIJQIqInQhAEQRAEQajwxJyIh0v0RAiCIAiCIAiCUCKiESEIgiAIgiAIQomI4UyCIAiCIAhChSfuWP1wiZ4IQRAEQRAEQRBKRvoPyM7OlsaMGSNlZ2eXdyglVpFjlyQRf3mqyLFLkoi/vFXk+Cty7JIk4i9PFTl2Sar48QsVi0ySpMd+KntqaioajYaUlBScnZ3LO5wSqcixg4i/PFXk2EHEX94qcvwVOXYQ8Zenihw7VPz4hYpFDGcSBEEQBEEQBKFERCNCEARBEARBEIQSEY0IQRAEQRAEQRBK5D/RiFAqlYwZMwalUlneoZRYRY4dRPzlqSLHDiL+8laR46/IsYOIvzxV5Nih4scvVCz/iYnVgiAIgiAIgiBYz3+iJ0IQBEEQBEEQBOsRjQhBEARBEARBEEpENCIEQRAEQRAEQSiR/1QjIjAwkB9//LG8wzCzbds2ZDIZycnJ5R2KVZTF8YwdOxZvb29kMhkrVqyw2n6L63H7jP4Lpk6dSpUqVZDL5Y/cd154tIiyUnzt2rVj1KhR5R1GuXocfg/K67dUePzYlHcAglCUqKgoxo0bx/Lly2nWrBmurq7lHZLwiEtNTWXkyJH88MMPPPHEE2g0mvIOSXhEibIilFSLFi2IiYmp0GUlJiZG/JYKViEaEcIj7cKFCwD07dsXmUxWztE8enJzc7GzsyvvMKzGGsdz9epVtFotPXv2xNfX10qRPf4kSUKv12Nj83j8LBTneCpiWXncPqeKxs7ODh8fn/IO44FU9PiFR0eFHc6UlpbGc889h0qlwtfXl8mTJ5t1td6+fZvevXvj4OBAUFAQ8+fPL7APmUzGH3/8Qffu3U35Fi9e/EBxSZLEN998Q9WqVXFwcKBevXosWbLE9PzatWsJCQnBwcGB9u3bc/nyZbPtx44dS3h4uFnajz/+SGBgYLFe/4UXXqBfv3589dVXeHt74+Liwrhx49DpdLz//vu4ubnh5+fHzJkzH/njGTt2LL179wZALpdbrRFR3p9RUe5XrgMDA/nyyy954YUX0Gg0jBgxAoAPP/yQkJAQHB0dqVq1Kp999hlarbZYr3n3eGbOnIm/vz9qtZrXXnsNvV7PN998g4+PD15eXkyYMOGRP55Zs2ZRp04dAKpWrYpMJivw+T2o+x1Tbm4uH3zwAZUrV0alUtG0aVO2bdtmto89e/bQpk0bHBwcqFKlCm+99RYZGRlWjROKLut3h2WsX7+eRo0aoVQq2blzZ5H7K8uy8rCP52GUleKy9uf0MMybN49GjRrh5OSEj48PgwYN4vbt26bnGzZsyPfff2963K9fP2xsbEhNTQUgNjYWmUxGdHT0Q4/dYDDw9ddfExwcjFKpxN/fnwkTJnD58mVkMhkLFy6kRYsW2NvbU7t2bbPvb3kPZypOnfrFF18waNAg1Go1lSpV4pdffjHbhxjOJFiNVEENHz5cCggIkDZt2iSdOHFC6t+/v+Tk5CS9/fbbkiRJUvfu3aWwsDBpz5490sGDB6UWLVpIDg4O0uTJk037ACR3d3dp2rRpUnR0tPS///1PUigU0unTp0sd1yeffCLVqFFDioiIkC5cuCD99ddfklKplLZt2yZdvXpVUiqV0ttvvy2dOXNGmjdvnuTt7S0BUlJSkiRJkjRmzBipXr16ZvucPHmyFBAQUKzXHzp0qOTk5CS98cYb0pkzZ6QZM2ZIgNS1a1dpwoQJ0tmzZ6UvvvhCsrW1la5evfpIH09aWpr0119/SYAUExMjxcTEFOs9eJSP6X7uV64DAgIkZ2dn6dtvv5XOnTsnnTt3TpIkSfriiy+k3bt3S5cuXZJWrVoleXt7S19//XWxXnPMmDGSWq2WBg4cKJ06dUpatWqVZGdnJ3Xt2lV68803pTNnzkgzZ86UAGnv3r2P9PFkZmZKmzZtkgBp//79UkxMjKTT6UoU84Me06BBg6QWLVpIO3bskM6fPy99++23klKplM6ePStJkiQdP35cUqvV0uTJk6WzZ89Ku3fvlurXry+98MILVo1Tkoou61u3bpUAqW7dutKGDRuk8+fPS/Hx8UXuryzLysM+nodRVorL2p9TWWnbtq2pnM+YMUNau3atdOHCBWnv3r1Ss2bNpO7du5vyvvvuu1KvXr0kSZIkg8Egubm5SR4eHtKaNWskSZKkBQsWSD4+Pg/9GCRJkj744APJ1dVVmjVrlnT+/Hlp586d0rRp06RLly5JgOTn5yctWbJEOn36tDR8+HDJycnJ9J7f/Tzu/h48bMWpU52cnKSJEydK0dHR0s8//ywpFAppw4YNpn0A0vLly8slfuHxUiEbEampqZKtra20ePFiU1pycrLk6Ogovf3221J0dLQESJGRkabno6KiJKBAI+LVV18123fTpk2l1157rVRxpaenS/b29tKePXvM0ocNGyY9++yz0scffyzVrFlTMhgMpuc+/PBDqzciAgICJL1eb0oLDQ2VWrdubXqs0+kklUol/f3334/88SxfvlyyZlv3UTimwtyvXEuS8QeiX79+993XN998IzVs2LBYrztmzBjJ0dFRSk1NNaV17dpVCgwMLFCOJk6cWMyjKb/jOXLkiARIly5dKnasxXW/Yzp//rwkk8mkGzdumG3XsWNH6eOPP5YkSZKef/556eWXXzZ7fufOnZJcLpeysrKsFuv9yvrdk6EVK1YUe59lVVaKoyyOpyzLSnGVxXGVlXsbEfnt379fAqS0tDRJkiRp1apVkkajkfR6vXT06FHJ09NTeuedd6T3339fkiRJevnll6Wnn376YYVukpqaKimVSmnatGkFnrvbiJg0aZIpTavVSn5+fqaLGOXZiChundqtWzez7Z5++mmzBp5oRAjWUiEHVV68eBGtVkuTJk1MaRqNhtDQUMA4GdfGxoZGjRqZnq9RowYuLi4F9tW8efMCj48ePVqquE6fPk12djadO3c2S8/NzaV+/fpkZWXRrFkzs2E5+V/fGmrXro1cnjdSzdvbm7CwMNNjhUKBu7u7WdezJY/K8VjTo3xM9yvXd91bru9asmQJP/74I+fPnyc9PR2dToezs3OxXzswMBAnJyfTY29vbxQKRYFydL8y86gcT1m53zEdPnwYSZIICQkx2y4nJwd3d3cADh06xPnz582GWEqShMFg4NKlS9SsWdMqsd6vrN9l6f0vSlmUleIoq+MpbxX1uI4cOcLYsWM5evQoiYmJGAwGwDjPpFatWrRp04a0tDSOHDnC7t27adu2Le3bt+fLL78EjMOCymOlp6ioKHJycujYsWOhee6t8++eS0RFRT2M8IpU3DrV0nmNWHlMKAsVshEhSRJAgTHyd9MLe764Srvd3Up0zZo1VK5c2ew5pVLJm2++ed99yOVyU/x3FXds+122trZmj2UymcW0u/EW5lE5Hmt6lI/pfuX6LpVKZfY4MjKSZ555hnHjxtG1a1c0Gg0LFy40G498P9YqM5biLo/jKSv3OyaDwYBCoeDQoUMoFAqzPGq12pTnlVde4a233iqwf39/f6vFer+yfnfRgvzv//2URVkpjrI6nvJWEY8rIyODLl260KVLF+bNm4enpydXr16la9eu5ObmAsaT2/DwcLZt28aePXvo0KEDrVu35ujRo5w7d46zZ8/Srl27hx67g4NDqbZ7FBb2KG6dasmjEL/w+KmQjYhq1apha2vL/v37qVKlCmBcqu/cuXO0bduWmjVrotPpOHjwoKnFHh0dbXEiVGRkJEOGDDF7fO/Vn5KoVasWSqWSq1ev0rZtW4vP55/MFBkZafbY09OT2NhYJEkyfelL2zPyoB6344FH+5juV64Ls3v3bgICAvj0009NaVeuXHngeB7U43Y8cP9jql+/Pnq9ntu3b9O6dWuL+2jQoAGnTp0iODi4TGO9X1m/e3JaUTxux3NXRTyuM2fOEB8fz6RJk0zfg4MHDxbI165dO7Zu3cq+fShLkacAAAUmSURBVPsYP348Li4u1KpViy+//BIvLy+r9bqVRPXq1XFwcGDz5s0MHz7cYp7IyEjatGkDgE6n49ChQ4wcOfJhhmlRcevU/L9ZkZGR1KhR46HGKvw3VMhGhJOTE0OHDjWtNuTl5cWYMWNMK/iEhobSrVs3RowYwdSpU7GxsWHUqFEWr0AsXryYRo0a0apVK+bPn8/+/fuZMWNGqeMaPXo077zzDgaDgVatWpGamsqePXtQq9W8+uqrfP/997z77ru88sorHDp0iFmzZpnto127dsTFxfHNN98wcOBAIiIiWLduXbkM5XjcjudRP6b7levCBAcHc/XqVRYuXEjjxo1Zs2YNy5cvf6BYrOFxOx64/zGFhITw3HPPMWTIEL7//nvq169PfHw8W7ZsoU6dOvTo0YMPP/yQZs2a8cYbbzBixAhUKhVRUVFs3LixwCoqDxprUWU9ICDAaq/1MDxux3NXRTwuf39/7Ozs+OWXX3j11Vc5efIkX3zxRYF87dq146effsLNzY1atWqZ0n755RcGDBjwsMMGwN7eng8//JAPPvgAOzs7WrZsSVxcHKdOnTINcfrtt9+oXr06NWvWZPLkySQlJfHSSy+VS7z3Km6dunv3br755hv69evHxo0bWbx4MWvWrCnHyIXH1kOeg2E1qamp0qBBgyRHR0fJx8dH+uGHH6QmTZpIH330kSRJkhQTEyP17NlTUiqVkr+/vzRnzhwpICCgwMTq3377TercubOkVCqlgICA+042vh+DwSD99NNPUmhoqGRrayt5enpKXbt2lbZv3y5JkiStXr1aCg4OlpRKpdS6dWvTSib3TtL6448/pCpVqkgqlUoaMmSINGHChBJNrO7bt69ZmqXJcPnfi0f1eKw9sfpROKai3K9cF/a5vf/++5K7u7ukVqulp59+Wpo8ebKk0WiK9ZqWJooXtxw9isdT1pNl73dMubm50ueffy4FBgZKtra2ko+Pj9S/f3/p+PHjpn3s379f6ty5s6RWqyWVSiXVrVtXmjBhgtVjLaqsl2aCaFmWleKw9vE8ChOrJcn6x1VW7v1cFyxYIAUGBkpKpVJq3ry5tGrVKgmQjhw5YsqfnJwsKRQKaeDAgaa0u3X6r7/++pCjz6PX66Uvv/xSCggIkGxtbSV/f3/pq6++Mk2sXrBggdS0aVPJzs5OqlmzprR582bTtuX9eRSnTh03bpz01FNPSY6OjpK3t7f0448/mu0DMbFasBKZJBVjMF0FkJGRQeXKlfn+++8ZNmxYsbaRyWQsX76cfv36lW1wglBKpSnXj7LH7Xjg8TwmQfgvunz5MkFBQRw5cqTAvYAeVfnrn8DAQEaNGlUuk9aF/54KOZwJjCtDnDlzhiZNmpCSksL48eMB452NBaGietzK9eN2PPB4HpMgCBWDqH+ER0mFvWM1wHfffUe9evXo1KkTGRkZ7Ny5Ew8Pj/IOq0yp1epC/x6Fu5iW1ON2PNZg7XJdu3btQt9jS3dyt7bH7Xjg8a17HoX31poet+MRBHh86x+h4nlshjP9V5w/f77Q5ypXrlzq5evKy+N2PI+iK1euFLoErbe3t9ma/xXB43Y8j5LH7b193I5HEAThUSIaEYIgCIIgCIIglEiFHs4kCIIgCIIgCMLDJxoRgiAIgiAIgiCUiGhECIIgCIIgCIJQIqIRIQiCIAiCIAhCiYhGhCAIgiAIgiAIJSIaEYIgCIIgCIIglIhoRAiCIAiCIAiCUCKiESEIgiAIgiAIQon8H5jouWGg/S4aAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation analysis\n",
    "print(\"\\nCorrelation Matrix:\")\n",
    "corr_matrix = data[['gdp', 'edu_m', 'edu_f', 'edu', 'gra_m', 'gra_f', 'gee', 'er_m', 'er_f', 'er', 'law', 'cpi', 'gpi']].corr()\n",
    "print(corr_matrix)\n",
    "\n",
    "# Plot correlation heatmap\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', linewidths=0.5)\n",
    "plt.title('Correlation Matrix Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Box Plot for different variables to analyze distribution, outliers, and spread\n",
    "plt.figure(figsize=(15, 10))\n",
    "\n",
    "#List of variables to plot\n",
    "variables = ['gdp', 'edu_m', 'edu_f', 'gra_m', 'gra_f', 'gee', 'er_m', 'er_f', 'law', 'cpi']\n",
    "\n",
    "#Loop over variables to create box plots\n",
    "for i, var in enumerate(variables):\n",
    "    plt.subplot(3, 4, i+1)  # Arrange subplots in a 3x4 grid\n",
    "    sns.boxplot(data=data, y=var)\n",
    "    plt.title(f'Box Plot of {var}')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (3)Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Variance Inflation Factor (VIF) before removing variables:\n",
      "    Variable          VIF\n",
      "0      edu_m     5.369602\n",
      "1      edu_f     9.181328\n",
      "2      gra_m     8.013070\n",
      "3      gra_f    11.918430\n",
      "4        gee     3.334180\n",
      "5       er_m     7.402679\n",
      "6       er_f     1.770175\n",
      "7        law     3.999863\n",
      "8        cpi     1.403122\n",
      "9  Intercept  1112.538442\n",
      "\n",
      "Variance Inflation Factor (VIF) after removing variables:\n",
      "    Variable         VIF\n",
      "0      edu_m    3.704832\n",
      "1      edu_f    7.766050\n",
      "2        gee    2.621380\n",
      "3       er_m    4.947926\n",
      "4       er_f    1.737251\n",
      "5        law    2.766147\n",
      "6        cpi    1.347268\n",
      "7  Intercept  648.737755\n"
     ]
    }
   ],
   "source": [
    "# Multicollinearity check using Variance Inflation Factor (VIF) before removing variables\n",
    "print(\"\\nVariance Inflation Factor (VIF) before removing variables:\")\n",
    "X = data[['edu_m', 'edu_f', 'gra_m', 'gra_f', 'gee', 'er_m', 'er_f', 'law', 'cpi']].copy()\n",
    "X.loc[:, 'Intercept'] = 1  # Add intercept for VIF calculation\n",
    "vif_data_before = pd.DataFrame()\n",
    "vif_data_before['Variable'] = X.columns\n",
    "vif_data_before['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "print(vif_data_before)\n",
    "\n",
    "# Remove highly collinear variables gra_m and gra_f\n",
    "data = data.drop(columns=['gra_m', 'gra_f'])\n",
    "\n",
    "# Multicollinearity check using Variance Inflation Factor (VIF) after removing variables\n",
    "print(\"\\nVariance Inflation Factor (VIF) after removing variables:\")\n",
    "X = data[['edu_m', 'edu_f', 'gee', 'er_m', 'er_f', 'law', 'cpi']].copy()\n",
    "X.loc[:, 'Intercept'] = 1  # Add intercept for VIF calculation\n",
    "vif_data_after = pd.DataFrame()\n",
    "vif_data_after['Variable'] = X.columns\n",
    "vif_data_after['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "print(vif_data_after)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (4)Model Selection--Hausman test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Hausman Test:\n",
      "Hausman Statistic: 433.2209073397926\n",
      "P-value: 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:599: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weighted_sum: DataFrame = frame.groupby(level=level).transform(\"sum\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:601: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  sum_weights: DataFrame = frame.groupby(level=level).transform(\"sum\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:599: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weighted_sum: DataFrame = frame.groupby(level=level).transform(\"sum\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:601: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  sum_weights: DataFrame = frame.groupby(level=level).transform(\"sum\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:685: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weighted_sum = frame.groupby(level=level).sum()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:687: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  sum_weights = frame.groupby(level=level).sum()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:685: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  weighted_sum = frame.groupby(level=level).sum()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:687: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  sum_weights = frame.groupby(level=level).sum()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n"
     ]
    }
   ],
   "source": [
    "# Hausman test for fixed vs. random effects\n",
    "# Using linearmodels library for panel data regression\n",
    "from linearmodels.panel import compare\n",
    "\n",
    "# Fixed effects model\n",
    "fixed_effects = PanelOLS.from_formula('log_gdp ~ edu_m + edu_f + gee + er_m + er_f + law + cpi + EntityEffects', data=data.set_index(['Country', 'Year'])).fit()\n",
    "\n",
    "# Random effects model\n",
    "random_effects = RandomEffects.from_formula('log_gdp ~ edu_m + edu_f + gee + er_m + er_f + law + cpi', data=data.set_index(['Country', 'Year'])).fit()\n",
    "\n",
    "# Standard Hausman test\n",
    "print(\"\\nHausman Test:\")\n",
    "fe_params = fixed_effects.params\n",
    "re_params = random_effects.params\n",
    "common_params = fe_params.index.intersection(re_params.index)\n",
    "\n",
    "b_diff = fe_params[common_params] - re_params[common_params]\n",
    "V_fe = fixed_effects.cov.loc[common_params, common_params]\n",
    "V_re = random_effects.cov.loc[common_params, common_params]\n",
    "V_diff = V_fe - V_re\n",
    "\n",
    "hausman_stat = b_diff.T @ np.linalg.inv(V_diff) @ b_diff\n",
    "p_value = 1 - chi2.cdf(hausman_stat, len(common_params))\n",
    "\n",
    "print(f\"Hausman Statistic: {hausman_stat}\")\n",
    "print(f\"P-value: {p_value}\")\n",
    "\n",
    "\n",
    "## Print regression results with p-values instead of standard errors\n",
    "#print(\"\\nFixed Effects Model Results:\")\n",
    "#print(fixed_effects.summary.tables[1].as_text().replace('Std. Err.', 'P-value'))\n",
    "\n",
    "#print(\"\\nRandom Effects Model Results:\")\n",
    "#print(random_effects.summary.tables[1].as_text().replace('Std. Err.', 'P-value'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (5) Visualisation of GDP per capita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot GDP trend over time for each country using seaborn\n",
    "plt.figure(figsize=(12, 6))\n",
    "for country in data['Country'].unique():\n",
    "    country_data = data[data['Country'] == country]\n",
    "    plt.plot(country_data['Year'], country_data['gdp'], label=country)\n",
    "\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('GDP')\n",
    "plt.title('GDP Trend Over Time by Country')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.Fixed Effect Model(no interactions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### \"Since both the general model and the sub-models exhibit heteroskedasticity and autocorrelation, robust standard errors will be used to estimate the regression models.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (1)General model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fixed Effects Regression Results:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                log_gdp   R-squared:                        0.3040\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -0.5362\n",
      "No. Observations:                 220   R-squared (Within):               0.3040\n",
      "Date:                Fri, Oct 25 2024   R-squared (Overall):             -0.5358\n",
      "Time:                        00:30:23   Log-likelihood                    82.261\n",
      "Cov. Estimator:                Robust                                           \n",
      "                                        F-statistic:                      14.776\n",
      "Entities:                          11   P-value                           0.0000\n",
      "Avg Obs:                       20.000   Distribution:                   F(6,203)\n",
      "Min Obs:                       20.000                                           \n",
      "Max Obs:                       20.000   F-statistic (robust):             19.819\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                      20   Distribution:                   F(6,203)\n",
      "Avg Obs:                       11.000                                           \n",
      "Min Obs:                       11.000                                           \n",
      "Max Obs:                       11.000                                           \n",
      "                                                                                \n",
      "                             Parameter Estimates                              \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "gpi           -0.0142     0.2111    -0.0673     0.9464     -0.4305      0.4021\n",
      "gee           -0.0321     0.0182    -1.7682     0.0785     -0.0680      0.0037\n",
      "er_m          -0.0618     0.0173    -3.5647     0.0005     -0.0960     -0.0276\n",
      "er_f           0.0405     0.0073     5.5436     0.0000      0.0261      0.0549\n",
      "law            0.1413     0.0973     1.4513     0.1482     -0.0507      0.3332\n",
      "cpi           -0.0150     0.0050    -3.0093     0.0029     -0.0249     -0.0052\n",
      "==============================================================================\n",
      "\n",
      "F-test for Poolability: 291.32\n",
      "P-value: 0.0000\n",
      "Distribution: F(10,203)\n",
      "\n",
      "Included effects: Entity\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n"
     ]
    }
   ],
   "source": [
    "# Fixed effects regression\n",
    "fe_model = PanelOLS.from_formula('log_gdp ~ gpi + gee + er_m + er_f + law + cpi + EntityEffects', \n",
    "                                 data=data.set_index(['Country', 'Year'])).fit(cov_type='robust')\n",
    "\n",
    "# Output the regression result\n",
    "print(\"\\nFixed Effects Regression Results:\")\n",
    "print(fe_model.summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Random effects regression\n",
    "#re_model = RandomEffects.from_formula('log_gdp ~ gpi + gee + er_m + er_f + law + cpi', data=data.set_index(['Country', 'Year'])).fit(cov_type='robust')\n",
    "\n",
    "## Output the regression result\n",
    "#print(\"\\nRandom Effects Regression Results:\")\n",
    "#print(re_model.summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (2)Sub-models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Male-specific model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fixed Effects Regression Results (Male) with Robust Standard Errors:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                log_gdp   R-squared:                        0.3619\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -0.7303\n",
      "No. Observations:                 220   R-squared (Within):               0.3619\n",
      "Date:                Fri, Oct 25 2024   R-squared (Overall):             -0.7298\n",
      "Time:                        00:30:24   Log-likelihood                    91.811\n",
      "Cov. Estimator:                Robust                                           \n",
      "                                        F-statistic:                      23.135\n",
      "Entities:                          11   P-value                           0.0000\n",
      "Avg Obs:                       20.000   Distribution:                   F(5,204)\n",
      "Min Obs:                       20.000                                           \n",
      "Max Obs:                       20.000   F-statistic (robust):             10.096\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                      20   Distribution:                   F(5,204)\n",
      "Avg Obs:                       11.000                                           \n",
      "Min Obs:                       11.000                                           \n",
      "Max Obs:                       11.000                                           \n",
      "                                                                                \n",
      "                             Parameter Estimates                              \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "edu_m          0.0115     0.0028     4.0919     0.0001      0.0060      0.0171\n",
      "gee           -0.0313     0.0186    -1.6819     0.0941     -0.0680      0.0054\n",
      "er_m          -0.0456     0.0117    -3.9138     0.0001     -0.0686     -0.0226\n",
      "law           -0.1060     0.0820    -1.2934     0.1973     -0.2676      0.0556\n",
      "cpi           -0.0134     0.0055    -2.4274     0.0161     -0.0242     -0.0025\n",
      "==============================================================================\n",
      "\n",
      "F-test for Poolability: 304.17\n",
      "P-value: 0.0000\n",
      "Distribution: F(10,204)\n",
      "\n",
      "Included effects: Entity\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n"
     ]
    }
   ],
   "source": [
    "# Fixed effects regression for male data only with robust standard errors\n",
    "fe_model_male = PanelOLS.from_formula('log_gdp ~ edu_m + gee + er_m + law + cpi + EntityEffects', data=data.set_index(['Country', 'Year'])).fit(cov_type='robust')\n",
    "\n",
    "# Output the regression result for male data\n",
    "print(\"\\nFixed Effects Regression Results (Male) with Robust Standard Errors:\")\n",
    "print(fe_model_male.summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Female-specific model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fixed Effects Regression Results (Female) with Robust Standard Errors:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                log_gdp   R-squared:                        0.3977\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -0.0254\n",
      "No. Observations:                 220   R-squared (Within):               0.3977\n",
      "Date:                Fri, Oct 25 2024   R-squared (Overall):             -0.0252\n",
      "Time:                        00:30:24   Log-likelihood                    98.176\n",
      "Cov. Estimator:                Robust                                           \n",
      "                                        F-statistic:                      26.943\n",
      "Entities:                          11   P-value                           0.0000\n",
      "Avg Obs:                       20.000   Distribution:                   F(5,204)\n",
      "Min Obs:                       20.000                                           \n",
      "Max Obs:                       20.000   F-statistic (robust):             32.390\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                      20   Distribution:                   F(5,204)\n",
      "Avg Obs:                       11.000                                           \n",
      "Min Obs:                       11.000                                           \n",
      "Max Obs:                       11.000                                           \n",
      "                                                                                \n",
      "                             Parameter Estimates                              \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "edu_f          0.0220     0.0031     7.0468     0.0000      0.0158      0.0281\n",
      "gee           -0.0050     0.0173    -0.2905     0.7718     -0.0392      0.0291\n",
      "er_f          -0.0121     0.0101    -1.2062     0.2291     -0.0319      0.0077\n",
      "law           -0.0294     0.0627    -0.4683     0.6401     -0.1531      0.0943\n",
      "cpi           -0.0214     0.0054    -3.9929     0.0001     -0.0320     -0.0108\n",
      "==============================================================================\n",
      "\n",
      "F-test for Poolability: 172.45\n",
      "P-value: 0.0000\n",
      "Distribution: F(10,204)\n",
      "\n",
      "Included effects: Entity\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n"
     ]
    }
   ],
   "source": [
    "# Fixed effects regression for female data only with robust standard errors\n",
    "fe_model_female = PanelOLS.from_formula('log_gdp ~ edu_f + gee + er_f + law + cpi + EntityEffects', data=data.set_index(['Country', 'Year'])).fit(cov_type='robust')\n",
    "\n",
    "# Output the regression result for female data\n",
    "print(\"\\nFixed Effects Regression Results (Female) with Robust Standard Errors:\")\n",
    "print(fe_model_female.summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.Fixed Effect Model(with interactions)---重点讲这个有交互项的模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create interaction terms\n",
    "data['law_er_m'] = data['law'] * data['er_m']\n",
    "data['law_er_f'] = data['law'] * data['er_f']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (1)General model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fixed Effects Regression Results with Interaction Terms:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                log_gdp   R-squared:                        0.5959\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -1.1861\n",
      "No. Observations:                 220   R-squared (Within):               0.5959\n",
      "Date:                Fri, Oct 25 2024   R-squared (Overall):             -1.1853\n",
      "Time:                        00:30:24   Log-likelihood                    142.06\n",
      "Cov. Estimator:                Robust                                           \n",
      "                                        F-statistic:                      32.765\n",
      "Entities:                          11   P-value                           0.0000\n",
      "Avg Obs:                       20.000   Distribution:                   F(9,200)\n",
      "Min Obs:                       20.000                                           \n",
      "Max Obs:                       20.000   F-statistic (robust):             30.266\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                      20   Distribution:                   F(9,200)\n",
      "Avg Obs:                       11.000                                           \n",
      "Min Obs:                       11.000                                           \n",
      "Max Obs:                       11.000                                           \n",
      "                                                                                \n",
      "                             Parameter Estimates                              \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "edu_m         -0.0006     0.0013    -0.4558     0.6490     -0.0033      0.0020\n",
      "edu_f          0.0153     0.0025     6.2264     0.0000      0.0105      0.0201\n",
      "gee            0.0098     0.0164     0.5993     0.5496     -0.0226      0.0422\n",
      "er_m          -0.0915     0.0152    -6.0012     0.0000     -0.1216     -0.0614\n",
      "er_f           0.0276     0.0082     3.3503     0.0010      0.0114      0.0439\n",
      "law           -4.4019     0.7071    -6.2249     0.0000     -5.7963     -3.0075\n",
      "cpi           -0.0023     0.0051    -0.4447     0.6570     -0.0124      0.0079\n",
      "law_er_m       0.1358     0.0178     7.6463     0.0000      0.1008      0.1709\n",
      "law_er_f      -0.0845     0.0137    -6.1724     0.0000     -0.1115     -0.0575\n",
      "==============================================================================\n",
      "\n",
      "F-test for Poolability: 206.49\n",
      "P-value: 0.0000\n",
      "Distribution: F(10,200)\n",
      "\n",
      "Included effects: Entity\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n"
     ]
    }
   ],
   "source": [
    "# Fixed effects regression with interaction terms\n",
    "fe_model1 = PanelOLS.from_formula('log_gdp ~ edu_m + edu_f + gee + er_m + er_f + law + cpi + law_er_m + law_er_f + EntityEffects',\n",
    "                                 data=data.set_index(['Country', 'Year'])).fit(cov_type='robust')\n",
    "\n",
    "# Output the regression result\n",
    "print(\"\\nFixed Effects Regression Results with Interaction Terms:\")\n",
    "print(fe_model1.summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (2)Sub-models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Male-specific model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fixed Effects Regression Results (Male) with Interaction Term and Robust Standard Errors:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                log_gdp   R-squared:                        0.4221\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -1.2424\n",
      "No. Observations:                 220   R-squared (Within):               0.4221\n",
      "Date:                Fri, Oct 25 2024   R-squared (Overall):             -1.2417\n",
      "Time:                        00:30:24   Log-likelihood                    102.72\n",
      "Cov. Estimator:                Robust                                           \n",
      "                                        F-statistic:                      24.712\n",
      "Entities:                          11   P-value                           0.0000\n",
      "Avg Obs:                       20.000   Distribution:                   F(6,203)\n",
      "Min Obs:                       20.000                                           \n",
      "Max Obs:                       20.000   F-statistic (robust):             10.349\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                      20   Distribution:                   F(6,203)\n",
      "Avg Obs:                       11.000                                           \n",
      "Min Obs:                       11.000                                           \n",
      "Max Obs:                       11.000                                           \n",
      "                                                                                \n",
      "                             Parameter Estimates                              \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "edu_m          0.0090     0.0026     3.4804     0.0006      0.0039      0.0141\n",
      "gee           -0.0221     0.0181    -1.2223     0.2230     -0.0578      0.0136\n",
      "er_m          -0.0696     0.0144    -4.8432     0.0000     -0.0979     -0.0413\n",
      "law           -2.8771     0.7431    -3.8715     0.0001     -4.3424     -1.4118\n",
      "cpi           -0.0061     0.0064    -0.9624     0.3370     -0.0187      0.0064\n",
      "law_er_m       0.0393     0.0107     3.6587     0.0003      0.0181      0.0604\n",
      "==============================================================================\n",
      "\n",
      "F-test for Poolability: 319.78\n",
      "P-value: 0.0000\n",
      "Distribution: F(10,203)\n",
      "\n",
      "Included effects: Entity\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n"
     ]
    }
   ],
   "source": [
    "# Fixed effects regression for male data only with interaction term and robust standard errors\n",
    "fe_model_male1 = PanelOLS.from_formula('log_gdp ~ edu_m + gee + er_m + law + cpi + law_er_m + EntityEffects', data=data.set_index(['Country', 'Year'])).fit(cov_type='robust')\n",
    "\n",
    "# Output the regression result for male data\n",
    "print(\"\\nFixed Effects Regression Results (Male) with Interaction Term and Robust Standard Errors:\")\n",
    "print(fe_model_male1.summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Female-specific model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fixed Effects Regression Results (Female) with Interaction Term and Robust Standard Errors:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                log_gdp   R-squared:                        0.4097\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -0.0584\n",
      "No. Observations:                 220   R-squared (Within):               0.4097\n",
      "Date:                Fri, Oct 25 2024   R-squared (Overall):             -0.0582\n",
      "Time:                        00:30:24   Log-likelihood                    100.39\n",
      "Cov. Estimator:                Robust                                           \n",
      "                                        F-statistic:                      23.483\n",
      "Entities:                          11   P-value                           0.0000\n",
      "Avg Obs:                       20.000   Distribution:                   F(6,203)\n",
      "Min Obs:                       20.000                                           \n",
      "Max Obs:                       20.000   F-statistic (robust):             27.212\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                      20   Distribution:                   F(6,203)\n",
      "Avg Obs:                       11.000                                           \n",
      "Min Obs:                       11.000                                           \n",
      "Max Obs:                       11.000                                           \n",
      "                                                                                \n",
      "                             Parameter Estimates                              \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "edu_f          0.0219     0.0031     7.0953     0.0000      0.0158      0.0280\n",
      "gee            0.0007     0.0175     0.0378     0.9699     -0.0338      0.0352\n",
      "er_f          -0.0152     0.0107    -1.4168     0.1581     -0.0362      0.0059\n",
      "law           -0.9119     0.3270    -2.7892     0.0058     -1.5566     -0.2673\n",
      "cpi           -0.0195     0.0057    -3.3939     0.0008     -0.0308     -0.0082\n",
      "law_er_f       0.0142     0.0052     2.7252     0.0070      0.0039      0.0245\n",
      "==============================================================================\n",
      "\n",
      "F-test for Poolability: 154.38\n",
      "P-value: 0.0000\n",
      "Distribution: F(10,203)\n",
      "\n",
      "Included effects: Entity\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n"
     ]
    }
   ],
   "source": [
    "# Fixed effects regression for female data only with interaction term and robust standard errors\n",
    "fe_model_female1 = PanelOLS.from_formula('log_gdp ~ edu_f + gee + er_f + law + cpi + law_er_f + EntityEffects', data=data.set_index(['Country', 'Year'])).fit(cov_type='robust')\n",
    "\n",
    "# Output the regression result for female data\n",
    "print(\"\\nFixed Effects Regression Results (Female) with Interaction Term and Robust Standard Errors:\")\n",
    "print(fe_model_female1.summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.After Modeling(tests)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (1)Breusch-Pagan Heteroskedasticity Test & Durbin-Watson Test-------不用汇报这一部分，直接在模型部分说一句‘使用稳健标准误进行回归估计防止异方差和自相关’即可"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Breusch-Pagan Test Statistic: 44.8679335067058\n",
      "P-value for Breusch-Pagan Test: 9.760324593439203e-07\n",
      "Durbin-Watson Statistic: 0.5721574252680062\n"
     ]
    }
   ],
   "source": [
    "# Extract the independent variables from the model and add a constant term\n",
    "exog = data[['edu_m', 'edu_f', 'gee', 'er_m', 'er_f', 'law', 'cpi', 'law_er_m', 'law_er_f']].values\n",
    "exog = sm.add_constant(exog)  # Add a constant term\n",
    "\n",
    "# Extract residuals\n",
    "residuals = fe_model1.resids\n",
    "\n",
    "# 1. Breusch-Pagan Heteroskedasticity Test\n",
    "bp_test = het_breuschpagan(residuals, exog)\n",
    "\n",
    "# Extract test results\n",
    "bp_test_stat = bp_test[0]  # LM statistic\n",
    "bp_p_value = bp_test[1]  # P-value for the LM statistic\n",
    "\n",
    "print(\"Breusch-Pagan Test Statistic:\", bp_test_stat)\n",
    "print(\"P-value for Breusch-Pagan Test:\", bp_p_value)\n",
    "\n",
    "# 2. Durbin-Watson Test\n",
    "dw_stat = durbin_watson(residuals)\n",
    "\n",
    "print(\"Durbin-Watson Statistic:\", dw_stat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Breusch-Pagan Test Statistic: 37.218442305275204\n",
      "P-value for Breusch-Pagan Test: 5.414881443936361e-07\n",
      "Durbin-Watson Statistic: 0.6024609108408521\n"
     ]
    }
   ],
   "source": [
    "# Extract the independent variables from the model and add a constant term\n",
    "exog = data[['edu_m', 'gee', 'er_m', 'law', 'cpi']].values\n",
    "exog = sm.add_constant(exog)  # Add a constant term\n",
    "\n",
    "# Extract residuals\n",
    "residuals = fe_model_male.resids\n",
    "\n",
    "# 1. Breusch-Pagan Heteroskedasticity Test\n",
    "bp_test = het_breuschpagan(residuals, exog)\n",
    "\n",
    "# Extract test results\n",
    "bp_test_stat = bp_test[0]  # LM statistic\n",
    "bp_p_value = bp_test[1]  # P-value for the LM statistic\n",
    "\n",
    "print(\"Breusch-Pagan Test Statistic:\", bp_test_stat)\n",
    "print(\"P-value for Breusch-Pagan Test:\", bp_p_value)\n",
    "\n",
    "# 2. Durbin-Watson Test\n",
    "dw_stat = durbin_watson(residuals)\n",
    "\n",
    "print(\"Durbin-Watson Statistic:\", dw_stat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Breusch-Pagan Test Statistic: 31.964915093755216\n",
      "P-value for Breusch-Pagan Test: 6.037063081787875e-06\n",
      "Durbin-Watson Statistic: 0.5286513064705765\n"
     ]
    }
   ],
   "source": [
    "# Extract the independent variables from the model and add a constant term\n",
    "exog = data[['edu_f', 'gee', 'er_f', 'law', 'cpi']].values\n",
    "exog = sm.add_constant(exog)  # Add a constant term\n",
    "\n",
    "# Extract residuals\n",
    "residuals = fe_model_female.resids\n",
    "\n",
    "# 1. Breusch-Pagan Heteroskedasticity Test\n",
    "bp_test = het_breuschpagan(residuals, exog)\n",
    "\n",
    "# Extract test results\n",
    "bp_test_stat = bp_test[0]  # LM statistic\n",
    "bp_p_value = bp_test[1]  # P-value for the LM statistic\n",
    "\n",
    "print(\"Breusch-Pagan Test Statistic:\", bp_test_stat)\n",
    "print(\"P-value for Breusch-Pagan Test:\", bp_p_value)\n",
    "\n",
    "# 2. Durbin-Watson Test\n",
    "dw_stat = durbin_watson(residuals)\n",
    "\n",
    "print(\"Durbin-Watson Statistic:\", dw_stat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (2)Robustness check-----直接汇报那张稳健性检验的三线表就行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fixed Effects Regression Results with Interaction Terms and kernel Standard Errors:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                log_gdp   R-squared:                        0.5959\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -1.1861\n",
      "No. Observations:                 220   R-squared (Within):               0.5959\n",
      "Date:                Fri, Oct 25 2024   R-squared (Overall):             -1.1853\n",
      "Time:                        00:30:24   Log-likelihood                    142.06\n",
      "Cov. Estimator:        Driscoll-Kraay                                           \n",
      "                                        F-statistic:                      32.765\n",
      "Entities:                          11   P-value                           0.0000\n",
      "Avg Obs:                       20.000   Distribution:                   F(9,200)\n",
      "Min Obs:                       20.000                                           \n",
      "Max Obs:                       20.000   F-statistic (robust):             167.74\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                      20   Distribution:                   F(9,200)\n",
      "Avg Obs:                       11.000                                           \n",
      "Min Obs:                       11.000                                           \n",
      "Max Obs:                       11.000                                           \n",
      "                                                                                \n",
      "                             Parameter Estimates                              \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "edu_m         -0.0006     0.0017    -0.3686     0.7128     -0.0039      0.0027\n",
      "edu_f          0.0153     0.0030     5.1818     0.0000      0.0095      0.0211\n",
      "gee            0.0098     0.0218     0.4524     0.6515     -0.0331      0.0528\n",
      "er_m          -0.0915     0.0158    -5.7990     0.0000     -0.1226     -0.0604\n",
      "er_f           0.0276     0.0089     3.1103     0.0021      0.0101      0.0451\n",
      "law           -4.4019     1.0176    -4.3256     0.0000     -6.4086     -2.3952\n",
      "cpi           -0.0023     0.0043    -0.5376     0.5914     -0.0107      0.0061\n",
      "law_er_m       0.1358     0.0192     7.0863     0.0000      0.0980      0.1736\n",
      "law_er_f      -0.0845     0.0147    -5.7615     0.0000     -0.1134     -0.0556\n",
      "==============================================================================\n",
      "\n",
      "F-test for Poolability: 206.49\n",
      "P-value: 0.0000\n",
      "Distribution: F(10,200)\n",
      "\n",
      "Included effects: Entity\n",
      "\n",
      "Fixed Effects Regression Results (Male) with Interaction Term and kernel Standard Errors:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                log_gdp   R-squared:                        0.4221\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -1.2424\n",
      "No. Observations:                 220   R-squared (Within):               0.4221\n",
      "Date:                Fri, Oct 25 2024   R-squared (Overall):             -1.2417\n",
      "Time:                        00:30:24   Log-likelihood                    102.72\n",
      "Cov. Estimator:        Driscoll-Kraay                                           \n",
      "                                        F-statistic:                      24.712\n",
      "Entities:                          11   P-value                           0.0000\n",
      "Avg Obs:                       20.000   Distribution:                   F(6,203)\n",
      "Min Obs:                       20.000                                           \n",
      "Max Obs:                       20.000   F-statistic (robust):             110.79\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                      20   Distribution:                   F(6,203)\n",
      "Avg Obs:                       11.000                                           \n",
      "Min Obs:                       11.000                                           \n",
      "Max Obs:                       11.000                                           \n",
      "                                                                                \n",
      "                             Parameter Estimates                              \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "edu_m          0.0090     0.0038     2.3611     0.0192      0.0015      0.0165\n",
      "gee           -0.0221     0.0213    -1.0401     0.2996     -0.0641      0.0198\n",
      "er_m          -0.0696     0.0138    -5.0498     0.0000     -0.0968     -0.0424\n",
      "law           -2.8771     0.8513    -3.3798     0.0009     -4.5555     -1.1987\n",
      "cpi           -0.0061     0.0049    -1.2502     0.2126     -0.0158      0.0035\n",
      "law_er_m       0.0393     0.0129     3.0433     0.0026      0.0138      0.0647\n",
      "==============================================================================\n",
      "\n",
      "F-test for Poolability: 319.78\n",
      "P-value: 0.0000\n",
      "Distribution: F(10,203)\n",
      "\n",
      "Included effects: Entity\n",
      "\n",
      "Fixed Effects Regression Results (Female) with Interaction Term and kernel Standard Errors:\n",
      "                          PanelOLS Estimation Summary                           \n",
      "================================================================================\n",
      "Dep. Variable:                log_gdp   R-squared:                        0.4097\n",
      "Estimator:                   PanelOLS   R-squared (Between):             -0.0584\n",
      "No. Observations:                 220   R-squared (Within):               0.4097\n",
      "Date:                Fri, Oct 25 2024   R-squared (Overall):             -0.0582\n",
      "Time:                        00:30:24   Log-likelihood                    100.39\n",
      "Cov. Estimator:        Driscoll-Kraay                                           \n",
      "                                        F-statistic:                      23.483\n",
      "Entities:                          11   P-value                           0.0000\n",
      "Avg Obs:                       20.000   Distribution:                   F(6,203)\n",
      "Min Obs:                       20.000                                           \n",
      "Max Obs:                       20.000   F-statistic (robust):             178.16\n",
      "                                        P-value                           0.0000\n",
      "Time periods:                      20   Distribution:                   F(6,203)\n",
      "Avg Obs:                       11.000                                           \n",
      "Min Obs:                       11.000                                           \n",
      "Max Obs:                       11.000                                           \n",
      "                                                                                \n",
      "                             Parameter Estimates                              \n",
      "==============================================================================\n",
      "            Parameter  Std. Err.     T-stat    P-value    Lower CI    Upper CI\n",
      "------------------------------------------------------------------------------\n",
      "edu_f          0.0219     0.0028     7.8298     0.0000      0.0164      0.0274\n",
      "gee            0.0007     0.0222     0.0298     0.9762     -0.0431      0.0444\n",
      "er_f          -0.0152     0.0057    -2.6818     0.0079     -0.0263     -0.0040\n",
      "law           -0.9119     0.4327    -2.1078     0.0363     -1.7650     -0.0589\n",
      "cpi           -0.0195     0.0054    -3.6021     0.0004     -0.0301     -0.0088\n",
      "law_er_f       0.0142     0.0074     1.9099     0.0576     -0.0005      0.0289\n",
      "==============================================================================\n",
      "\n",
      "F-test for Poolability: 154.38\n",
      "P-value: 0.0000\n",
      "Distribution: F(10,203)\n",
      "\n",
      "Included effects: Entity\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n"
     ]
    }
   ],
   "source": [
    "# Fixed effects regression with interaction terms\n",
    "fe_model = PanelOLS.from_formula('log_gdp ~ edu_m + edu_f + gee + er_m + er_f + law + cpi + law_er_m + law_er_f + EntityEffects', \n",
    "                                 data=data.set_index(['Country', 'Year'])).fit(cov_type='kernel')\n",
    "# Output the regression result\n",
    "print(\"\\nFixed Effects Regression Results with Interaction Terms and kernel Standard Errors:\")\n",
    "print(fe_model.summary)\n",
    "\n",
    "# Fixed effects regression for male data only with interaction term and kernel standard errors\n",
    "fe_model_male = PanelOLS.from_formula('log_gdp ~ edu_m + gee + er_m + law + cpi + law_er_m + EntityEffects', data=data.set_index(['Country', 'Year'])).fit(cov_type='kernel')\n",
    "# Output the regression result for male data\n",
    "print(\"\\nFixed Effects Regression Results (Male) with Interaction Term and kernel Standard Errors:\")\n",
    "print(fe_model_male.summary)\n",
    "\n",
    "# Fixed effects regression for female data only with interaction term and kernel standard errors\n",
    "fe_model_female = PanelOLS.from_formula('log_gdp ~ edu_f + gee + er_f + law + cpi + law_er_f + EntityEffects', data=data.set_index(['Country', 'Year'])).fit(cov_type='kernel')\n",
    "# Output the regression result for female data\n",
    "print(\"\\nFixed Effects Regression Results (Female) with Interaction Term and kernel Standard Errors:\")\n",
    "print(fe_model_female.summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Regression Coefficients Table:\n",
      "         general model (robust) general model (kernel) male model (robust)  \\\n",
      "edu_m          -0.0006 (0.7128)        0.0090 (0.0192)                 NaN   \n",
      "law_er_m        0.1358 (0.0000)        0.0393 (0.0026)                 NaN   \n",
      "gee             0.0098 (0.6515)       -0.0221 (0.2996)     0.0007 (0.9762)   \n",
      "law_er_f       -0.0845 (0.0000)                    NaN     0.0142 (0.0576)   \n",
      "cpi            -0.0023 (0.5914)       -0.0061 (0.2126)    -0.0195 (0.0004)   \n",
      "er_m           -0.0915 (0.0000)       -0.0696 (0.0000)                 NaN   \n",
      "law            -4.4019 (0.0000)       -2.8771 (0.0009)    -0.9119 (0.0363)   \n",
      "er_f            0.0276 (0.0021)                    NaN    -0.0152 (0.0079)   \n",
      "edu_f           0.0153 (0.0000)                    NaN     0.0219 (0.0000)   \n",
      "\n",
      "         male model (kernel) female model (robust) female model (kernel)  \n",
      "edu_m       -0.0006 (0.6490)       0.0090 (0.0006)                   NaN  \n",
      "law_er_m     0.1358 (0.0000)       0.0393 (0.0003)                   NaN  \n",
      "gee          0.0098 (0.5496)      -0.0221 (0.2230)       0.0007 (0.9699)  \n",
      "law_er_f    -0.0845 (0.0000)                   NaN       0.0142 (0.0070)  \n",
      "cpi         -0.0023 (0.6570)      -0.0061 (0.3370)      -0.0195 (0.0008)  \n",
      "er_m        -0.0915 (0.0000)      -0.0696 (0.0000)                   NaN  \n",
      "law         -4.4019 (0.0000)      -2.8771 (0.0001)      -0.9119 (0.0058)  \n",
      "er_f         0.0276 (0.0010)                   NaN      -0.0152 (0.1581)  \n",
      "edu_f        0.0153 (0.0000)                   NaN       0.0219 (0.0000)  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:640: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  out = self._frame.groupby(level=level).count()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:680: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  mu = self._frame.groupby(level=level).mean()\n",
      "d:\\LeStoreDownload\\anaconda\\Lib\\site-packages\\linearmodels\\panel\\data.py:590: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  group_mu = self._frame.groupby(level=level).transform(\"mean\")\n"
     ]
    }
   ],
   "source": [
    "# Fit models and collect results\n",
    "model_summaries = []\n",
    "\n",
    "## kernal\n",
    "# Fixed effects regression with interaction terms\n",
    "fe_model_kernel = PanelOLS.from_formula('log_gdp ~ edu_m + edu_f + gee + er_m + er_f + law + cpi + law_er_m + law_er_f + EntityEffects', data=data.set_index(['Country', 'Year'])).fit(cov_type='kernel')\n",
    "model_summaries.append(fe_model_kernel)\n",
    "\n",
    "# Fixed effects regression for male data only with interaction term and kernel standard errors\n",
    "fe_model_male_kernel = PanelOLS.from_formula('log_gdp ~ edu_m + gee + er_m + law + cpi + law_er_m + EntityEffects', data=data.set_index(['Country', 'Year'])).fit(cov_type='kernel')\n",
    "model_summaries.append(fe_model_male_kernel)\n",
    "\n",
    "# Fixed effects regression for female data only with interaction term and kernel standard errors\n",
    "fe_model_female_kernel = PanelOLS.from_formula('log_gdp ~ edu_f + gee + er_f + law + cpi + law_er_f + EntityEffects', data=data.set_index(['Country', 'Year'])).fit(cov_type='kernel')\n",
    "model_summaries.append(fe_model_female_kernel)\n",
    "\n",
    "## robust\n",
    "# Fixed effects regression with interaction terms\n",
    "fe_model_robust = PanelOLS.from_formula('log_gdp ~ edu_m + edu_f + gee + er_m + er_f + law + cpi + law_er_m + law_er_f + EntityEffects', data=data.set_index(['Country', 'Year'])).fit(cov_type='robust')\n",
    "model_summaries.append(fe_model_robust)\n",
    "\n",
    "# Fixed effects regression for male data only with interaction term and robust standard errors\n",
    "fe_model_male_robust = PanelOLS.from_formula('log_gdp ~ edu_m + gee + er_m + law + cpi + law_er_m + EntityEffects', data=data.set_index(['Country', 'Year'])).fit(cov_type='robust')\n",
    "model_summaries.append(fe_model_male_robust)\n",
    "\n",
    "# Fixed effects regression for female data only with interaction term and robust standard errors\n",
    "fe_model_female_robust = PanelOLS.from_formula('log_gdp ~ edu_f + gee + er_f + law + cpi + law_er_f + EntityEffects', data=data.set_index(['Country', 'Year'])).fit(cov_type='robust')\n",
    "model_summaries.append(fe_model_female_robust)\n",
    "\n",
    "# Create a table to display results\n",
    "all_params = set()\n",
    "for model in model_summaries:\n",
    "    all_params.update(model.params.index)\n",
    "all_params = list(all_params)\n",
    "\n",
    "results_table = pd.DataFrame(index=all_params, columns=['general model (robust)', 'general model (kernel)', 'male model (robust)', 'male model (kernel)', 'female model (robust)', 'female model (kernel)'])\n",
    "\n",
    "for i, model in enumerate(model_summaries):\n",
    "    # Extract coefficients and p-values\n",
    "    coefs = model.params\n",
    "    pvals = model.pvalues\n",
    "    for param in all_params:\n",
    "        if param in coefs.index:\n",
    "            results_table.iloc[all_params.index(param), i] = f\"{coefs[param]:.4f} ({pvals[param]:.4f})\"\n",
    "\n",
    "# Display the table\n",
    "print(\"\\nRegression Coefficients Table:\")\n",
    "print(results_table)"
   ]
  }
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